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# OAR Box 

# 1111 Prepped byRyan Dugan



## Document

Number: 

# •^Yd ll-A- 2 Docket Number: 

# A-83-2 2

Printed4/15/2008 1:25:18 PM Sheet 111 of 118P. 2P. 3

Fina l Repor t
A NEXAMINATIO NO F AI R QUALIT Y DATACOMPLETENES S REQUIREMENT S

SYSAPP-84/07 6 2 9 May198 4

Prepare d fo r Monitorin g and Report s Branc h Offic e o f Ai r Qualit yPlannin g and Standard s U.S . Environmenta l Protectio n Agenc y Researc hTriangl e Park , NC 2771 1

Prepare d b

y A . D . Thral l J . L . Baptist a C . S . Burto n System s Applications , Inc , 10 1 Luca s Valle yRoa d Sa n Rafael , CA9490 3

6830 6 e f 831U6 TP. 4



### DISCLAIME R

Thi s repor t ha s bee nreviewe db y th e Offic e o f Ai r Qualit y Plannin g andStandards , U.S . Environmenta l Protectio nAgency , andapprove dfo r publication . Approva l doe s no t signif y tha t th e content s necessaril y reflec t th e view s andpolicie s o f th e U.S . Environmenta l Protectio n Agency , no r doe s mentio no f trad e name s o r commercia l product s constitut e endorsemen t o r recommendatio nfo r use .



## nP. 5



### ACKNOWLEDGMENT S

Th e author s ar e gratefu l t o Scot t Chapli nfo r hi s researc ho ncurren t dat a completenes s criteri a an dt o Mik e Harper , Bell e Hudischewskyj , an dGary Lundber gfo r assistanc e incomputing . Th e author s als o thankBil l Hunt , Nei l Frank , an

dTom Curra no f th e U.S . Environmenta l Protectio nAgenc y fo r thei r thoughtfu l comments .

83i<+ 6 1 ii iP. 6



### CONTENT S

Acknow l edgment s ' ii i Lis t o f Illustration s v i Lis t o f Table s x Notat i o nx i i i Executiv e Summar yx v 1 INTRODUCTIO N1

Backgroun d1 Methodolog yan d Organizatio no f Result s 3 2 STATISTICA LPROCEDURE S FO R DETERMININ G TH E AI R QUALIT

YSTATUS O

F ASIT E 6 Annua l o rQuarterl y(Long-Term ) Average s 6 Short-Ter m Concentration s 1 0 Th e Observe d Numbe ro fShort-Ter m Exceedance s 1 0 Th e Estimate d Numbe ro fShort-Ter m Exceedance s 1 5 Curren tPractic e i n Determinin g th e Short-Ter m Attainmen tStatu s o fa Sit e 1 9 Limitin g th e Maximu m Probabilit yo fTyp e I Erro r1 9 Summar yo fResult s an d Alternativ e Approache s 27 

### 3 EMPIRICA L ESTIMATIO NO F TH E PROBABILIT YO F MISCLASSIFYIN GATTAINMEN T STATU S 3 1 Long-Ter mAmb i en t Standard s 3 1 Short-Ter m

Amb i en t Standard s 4 3 Summar y5 3

83i46 r i i vP. 7

STATISTICA

L ESTIMATIO

NO F TH E STATU S AN

D TREND S I

NNATIONA

L AI R QUALIT

Y AQuestion o f Bia s , Varianc e o f the Estimators , 5 A NEMPIRICA

L ASSESSMEN

T OF TH E DAT

ACOMPLETENES S NEEDE D TO RELIABLY ESTIMAT E NATIONA L TREND S Sulfu rDioxid e Carbon Monoxid e , Nitroge n Dioxid e Ozon e , Lea d Tota l Suspende d Particulate , Summ a r y, Reference s , Appendi xASULFU

R DIOXID E DESCRIPTIV E STATISTIC S Appendi xB CARBO

NMONOXID E DESCRIPTIV E STATISTIC S Appendi xC NITROGE NDIOXID E DESCRIPTIV E STATISTIC S Appendi xD OZON

E DESCRIPTIV

E STATISTIC S Appendi xE LEA

D DESCRIPTIV

E STATISTIC S Append i xF PARTICULAT E MATTE R DESCRIPTIV E STATISTIC S 5 5 5 5 5 6

5 8 5 8 5 8 6 5 6 5 7 2 7 2 7 2 8 0

Appendi xGSUMMAR

Y O F CURREN

T DAT ACOMPLETENES S CRITERI A

83U6 r lP. 8

ILLUSTRATIONS



### 2- 1 Binomia l approximatio n t o a an d 3 i n th e one-exceedance 

## case , r = 1 2 1 2- 2 Binomia l approximatio n t o a an d 3 i n th e three-exceedanc e case , r= 3 23 2- 3 Binomia l approximatio n t o a an d 3 fo r r = 1 an

d a specificatio n o f a g = 0. 2 '.. . 2 6 2- 4 Binomia l approximatio n t o a an d 3 fo r r = 3 an

d a specificatio n o f a g = 0. 2 2 8 3- 1 Lead : the probabilit yo f misjudging actua l attainmen t statu s a s determine d b

yth e mean , th e coefficien t o f variation , an d the proportion o f dat a completenes s 3 6 3- 2 TSP , annua l geometri c mean : th e probabilit yo f misjudging actua l attainmen t statu s a s determine d b ythe natura l log o f the geometri c mean , th e coefficien t o f variation , an d the proportion o f dat a completenes s 3 7 3- 3 TSP , annua l arithmeti c mean : th e probabilit yo f misjudging actua l attainmen t statu s a s determine d b ythe mean , th e coefficien t o f variation , an d th e proportion o f dat a comp l etenes s 3 8 3- 4 Estimate d probabilitie s a an d 3 o f misjudging attainmen t o f th e 24-hour sulfur dioxid e standard a s function s o f th e proport i o

n o f dat a comp l etenes s 4 6 3- 5 Estimated probabilitie s a an d 3 o f misjudging attainmen t o f th e one-hour carbo n monoxid e standard a s function s o f the proportion o f dat a completenes s 4 7 3- 6 Estimate d probabilitie s a an d 3 o f misjudging th e 8-hou r carbo n monoxid e standard a s function s o f th e proportio n o f dat a comp l etenes s 4 8

83146 T 1 v iP. 9

3- 7 Estimate d probabilitie s a an d 3 o fmisjudgin g attainmen t o fth e maximu m dail yone-hou rozon e standar d as function s o fth e proportio n o fdat a completenes s 4 9 3- 8 Estimate d probabilitie s a an d 3 o fmisjudgin g attainmen t o fth e 24-hou rTSP standar d as function s o fth e proportio n o fdat a comp l etenes s 5 0 5- 1 Estimate d long-ter m sulfu rdioxid e ai rqualit yparameter s a s function s o fth e require d dat a completenes s 6 0 5- 2 Estimate d short-ter m sulfu rdioxid e ai rqualit yparameter s as function s o fth e require d dat a completenes s 6 2 5- 3 Estimate d one-hou rcarbo n monoxid e ai rqualit yparameter s as function s o fth e require d dat a completenes s 6 4 5- 4 Estimate d 8-hou rcarbo n monoxid e ai rqualit yparameter s as function s o fth e require d dat a completenes s 67 5- 5 Estimate d long-ter m nitroge n dioxid e ai rqualit yparameter s as function s o fth e require d dat a completenes s 6 9 5- 6 Estimate d annua l ozon e ai rqualit yparameter s as function s o fth e require d dat a completenes s 7 1 5- 7 Estimate d long-ter m lea d ai rqualit yparameter s as function s o fth e require d dat a completenes s 7 4 5- 8 Estimate d long-ter m TSP ai rqualit yparameter s a s function s o fth e require d dat a completenes s 7 6 5- 9 Estimate d short-ter m TSP ai rqualit yparameter s as function s o fth e require d dat a completenes s 7 8 Appendi xA : Sulfu rDioxid e A- l Histogra m o fth e percentag e o fdat a completenes s fo r site s reportin g sulfu rdioxid e concentration s i n 198 1 A- 2 Scatte rplot s o fambien tai rqualit ystatistic s versu s th e percentag e o ftota l hour s recorde d i n 198 1 b ysite s reportin g sulfu rdioxid e concentration s A- 3 Histogra m o fexpecte d primar yexceedance s fo rsite s reportin g 24-hou rsulfu rdioxid e concentration s i n 198 1

83me r1 vi iA- 4 Scatte r plo t o f expecte d primar yexceedance s versu s th e percentag e o f tota l day s recorde d i n 198 1 b ysite s reportin g 24-hou r sulfu r dioxid e concentration s Appendi xB : Carbo n Monoxid e B- l Histogra mo f th e percentag e o f dat a completenes s fo r site s reportin g carbo n monoxid e concentration s i n 198 1
B- 2 Histogra mo f expecte d primar yexceedance s fo r site s reportin g one-hou r carbo n monoxid e concentration s i n 198 1
B- 3 Scatte r plo t o f th e numbe r o f expecte d primar yexceedance s versu s th e percentag e o f tota l hour s recorde d i n 198 1 b y site s reportin g one-hou r carbo n monoxid e concentration s B- 4 Histogra mo f expecte d primar yexceedance s fo r site s reportin g 8-hou r carbo n monoxid e concentration s i n 198 1
B- 5 Scatte r plo t o f th e numbe r o f expecte d primar yexceedance s versu s th e percentag e o f tota l hour s recorde d i n 198 1 b y site s reportin g 8-hou r carbo n monoxid e concentration s Appendi xC : Nitroge n Dioxid e C- 1 Histogra mo f th e percentag e o f dat a completenes s fo r site s reportin g nitroge n dioxid e concentration s i n 198 1 C- 2 Scatte r plot s o f ambien t ai r qualit ystatistic s versu s th e percentag e o f tota l hour s recorde d i n 198 1 b ysite s reportin g nitroge n dioxid e concentration s i n 198 1 Appendi xD : Ozon e D- 1 Histogra mo f th e percentag e o f dat a completenes s fo r site s reportin g ozon e concentration s i n 198 1
D- 2 Histogra mo f th e numbe r o f expecte d primar yexceedance s fo r site s reportin g ozon e concentration s i n 198 1
D- 3 Scatte r plo t o f th e numbe r o f expecte d primar yexceedance s versu s th e percentag e o f dat a completenes s fo r site s reportin g ozon e concentration s i n 198 1

83146 T i v mP.1 1



### Appendi xE : Lea d E- l Histogra m

o f th e percentag e o f dat a completenes s fo r site s reportin g lea dconcentration s i n198 1 E- 2 Scatte r plot s o f ambien t ai r qualit ystatistic s versu s th e percentag e o f dat a completenes s fo r site s reportin g lea d concentration s i n198 1 Appendi xF : Tota l Suspende dParticulat e F- l Histogra mo f th e percentag e o f dat a completenes s fo r site s reportin g TS P concentration s i n198 1 F- 2 Scatte r plot s o f ambien t ai r qualit ystatistic s versu s th e percentag e o f dat a completenes s fo r site s reportin g TS P concentration s i n198 1 F- 3 Histogra mo f th e numbe r o f expecte dprimar yexceedance s fo r site s reportin g TS P concentration s i n198 1 F- 4 Scatte r plo t o f th e numbe r o f expecte dprimar yexceedance s versu s th e percentag e o f dat a completenes s fo r site s reportin g TS P concentration s i n198 1

83m6 r i i xP.1 2

TABLE S

2- 1 The required proportion o f dat a completenes s a s a function o f th e tota l numbe r o f possibl e measurements , th e coefficien t o f variation , an

d the prescribe d maximu mrelative erro r occurring wit h9 5 percen t probability8 2- 2 Relative erro r fo r dat a completenes s proportion s p ^ = 0.5 0 an d p. j = 0.7 5 9 2- 3 The proportion o f dat a completenes s required t o limi t th e ris k t o 3 o f no t detecting r + 1 exceedance s 1 4 2- 4 The maximu

mprobabilityo f a Typ e I erro r an d the binomia l approximatio n t o th e probability , a s function s o f th e pro portion o f dat a completenes s 1 8 2- 5 Binomia l approximation s t o probabilitie s a an d 3 in th e one-exceedanc e case , r = 1 2 0 2- 6 Binomia l approximation s t o probabilitie s a an d 3 in the three-exceedanc e case , r = 3 2 2 2- 7 Attainmen t statu s a s a functio n o f observe d exceedance s an d the proportion o f dat a completenes s 2 5 2- 8 Attainmen t statu s fo r the one-exceedanc e for mo f th e standard ( r = 1 ) 2 5 3- 1 Estimate d probabilit ytha ta site' s attainmen tstatu s fo r long-ter m standard s i s misclassifie d as a functio n o f minimu m dat a completenes s proportio n 3 4 3- 2 Estimate d probabilit ytha ta site' s attainmen tstatu s fo r short-ter m standard s i s misclassifie d a s a functio n o f minimu m dat a completenes s proportio n 5 1

831<+6 r1P.1 3



### 5- 1 Estimate d composit e annua l averag e o fone-hou rsulfu rdioxid e and it s estimate d varianc e a s function s o fth e require d percentag e o fdat a completenes s 59 5- 2 Estimate d mea n annua l rat e o f24-hou rsulfu rdioxid e exceedanc e and it s estimate d varianc e a s function s o f th e require d percentag e o f dat a completenes s 6 1 5- 3 Estimate d mea n annua l rat e o f one-hou r carbo n monoxid e exceedanc e and it s estimate d varianc e a s function s o f th e require d percentag e o f dat a completenes s 6 3 5- 4 Estimate d mea n annua l rat e o f 8-hou r carbo n monoxid e exceedanc e and it s estimate d varianc e a s function s o f th e require d percentag e o f dat a completenes s 6 6 5- 5 Estimate d composit e annua l averag e o f one-hou r nitroge n dioxid e and it s estimate d varianc e a s function s o f th e require d percentag e o f dat a completenes s 6 8 5- 6 Estimate d mea n annua l rat e o f maximumdail yone-hou r ozon e exceedanc e an d it s estimate d varianc e a s function s o f th e require d percentag e o f dat a completenes s 7 0 5- 7 Estimate d composit e averag e o f maximumquarterl ylea d and it s estimate d varianc e a s function s o f th e require d percentag e o f dat a completenes s 7 3 5- 8 Estimate d composit e averag e o f 24-hou r TS P and it s estimate d varianc e a s function s o f th e require d percentag e o f dat a comp l etenes s 7 5 5- 9 Estimate d mea n annua l rat e o f 24-hou r TS P exceedanc e and it s estimate d varianc e a s function s o f th e require d percentag e o f dat a completenes s 7 7
Appendi xA : Sulfu r Dioxid e A- l Numbe r o f site s recordin g one-hou r sulfu r dioxid e durin g a t leas t 7 5 percen t o f th e tota l possibl e hour s i n 198 1
A- 2 Numbe r o f site s recordin g one-hou r sulfu r dioxid e durin g a t leas t 5 0 percen t o f th e tota l possibl e hour s i n 198 1

83iit6 r l x iP.1 4

A- 3 Frequenc ytabl e o f th e percentag e o f tota l day s recorde d i n 198 1 versu s expecte d primar yexceedance s fo r site s reportin g 24-hou r sulfu r dioxid e average s Appendi xB : Carbo n Monoxid e B- l Numbe r o f site s recordin g carbo n monoxid e durin g a t leas t 7 5 percen t o f th e tota l possibl e hour s i n 198 1
B- 2 Numbe r o f site s recordin g carbo n monoxid e durin g a t leas t 5 0 percen t o f th e tota l possibl e hour s i n 198 1
B- 3 Frequenc ytabl e o f th e percentag e o f tota l day s recorde d i n 198 1 versu s expecte d primar yexceedance s fo r site s reportin g one-hou r carbo n monoxid e average s B- 4 Frequenc ytabl e o f th e percentag e o f dat a completenes s i n 198 1 versu s expecte d primar yexceedance s fo r site s reportin g 8-hou r carbo n monoxid e average s Appendi xD : Ozon e D- 1 Frequenc ytabl e o f th e percentag e o f dat a completenes s i n 198 1 versu s expecte d primar yexceedance s fo r site s reportin g maximu mdail yozon e concentration s Appendi xF : Tota l Suspende d Particulat e F- l Numbe r o f site s recordin g 24-hou r TS P durin g a t leas t 7 5 percen t o f th e schedule d monitorin g day s i n 198 1
F- 2 Numbe r o f site s recordin g 24-hou r TS P durin g a t leas t 5 0 percen t o f th e schedule d monitorin g day s i n 198 1
F- 3 Frequenc ytabl e o f th e percentag e o f tota l day s recorde d i n 198 1 versu s expecte d primar yexceedance s fo r site s reportin g 24-hou r TS P average s Appendi xG : Curren t Dat a Completenes s Criteri a G- 1 Nationa l Aerometri c Dat a Bank' s criteri a fo r validit y o f dat a G- 2 Californi a Ai r Resource s Board' s criteri a fo r representa
tivenes s o f ai r qualit ymeasurement s and statistic s G- 3 Indian a Ai r Pollutio n Contro l Division' s criteri a fo r completenes s o f dat a
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x1 1P.1 5

NOTATIO N

Fixe do r controlle dparameter s

N = numbe r o f possibl e measurement s ina yea r P Q= require ddat a completenes s proportio no r percentag e
e = specifie dtoleranc e fo r estimatin ga site-specifi c long-term averag e concentratio n

r = th e maximum

numbe r o f time s short-term concentration s a t a sit e may

excee da referenc e concentratio nwithou t violatin g a give nshort-term

ai r qualit y

standar d

a = probabilit y

tha t th e attainmen t statu s o f a sit e wil l be incorrectl y

determine da s nonattainmen t 3 = probabilit y

tha t a site' s failur e t o attai nanai r qualit y standar dwil l go undetecte d Site-specifi c quantitie s ( i = sit e index )

p. j = proportio no f possibl e measurement s actuall y recorde ddurin g th e yea r

u - = averag e o f th e p n - N concentration s recorde ddurin gth e yea r

u-j = averag e o f al l N concentration s tha t coul dhav e bee n recorde ddurin gth e yea r a- = standar ddeviatio no f th e p^ N concentration s recorde ddurin g th e yea r 0 ^ = standar ddeviatio no f al l N concentration s tha t coul dhav e bee nrecorde ddurin gth e yea r k. j = th e numbe r o f time s a sit e i s reporte dt o hav e exceede da short-term

referenc e concentratio n

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### \. = th e estimate dnumbe r o f short-term exceedance s
x i 

### = th e actua l numbe r o f short-term exceedance s (equa l t o k^ whe np., - = 1 ) Sampl e an

dpopulatio nquantitie s IT = se t o f indice s i o f site s comprisin gth e sampl e v(pg ) = numbe r o f site s inth e sampl e whos e dat a completenes s

proportio nequal s o r exceed s p g u(p 0 

### ) = sampl e averag e o f w- acros s site s wit hp . > p Q 

### u(pg ) = populatio nmea no f u - acros s site s wit hp . > P Q
u= populatio nmea no f u ^ "2 "2 

### a (p n 

### ) = sampl e averag e o f varianc e estimate s o i 

### acros s site s

wit hp . >

p Q 

### 2 2 o (p 0 

### ) = populatio nmea no f variance s o ^ acros s site s wit hp . > p Q 

### 2 2 a = populatio nmea no f variance s a i 

### x(p n 

### ) = sampl e averag e o f estimate dsite-specifi c exceedance s (x^ )

fo r thos e site s wit hp . > p Q 

### x(p Q 

### ) = populatio nmea no f site-specifi c exceedance s (x.. ) fo r thos e

site s wit hp . >

p Q 

### X= populatio nmea no f site-specifi c exceedance s

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EXECUTIV E SUMMARY

PURPOS E ANDSCOP E OF TH E STUD Y
Th e U.S . Environmenta l Protectio nAgenc y (EPA ) store s ambien t ai r qualit y data , obtaine dfrom a nationwid e networ ko f monitorin gstations , i nth e Nationa l Aerometri c Dat a Bank(NADB) . Statistic s summarizin gth e ai r qualit y at eac hmonitorin gstatio nar e als o store dinth e NADB . Thes e summary statistic s ar e mad e availabl e t o th e governmen t an dpubli c provide dtha t certai ndat a validit y criteri a ar e met , chie f amon gthes e bein grequirement s fo r dat a completenes s (give ni nAppendi xG) .
Thi s dat a screenin gpolic y help s t o ensur e tha t NADB summar y statistic s ar e reliable . Moreover , th e polic y o f usin gon e se t o f dat a validit y criteri a regardles s o f th e intende dus e o f th e dat a i s necessar y sinc e th e EP A mus t respondquickl y t o numerou s request s fo r NADB data . It i s possible , however , tha t some informatio nma y b e unnecessaril y

los t t o th e genera l NADB use r becaus e o f th e stringenc y o f th e criteri a fo r dat a validity . Nevertheless , stringen t criteri a may be neede dfo r some purposes , notabl y

fo r determinin gwhethe r Nationa l Ambien t Ai r Qualit y Standard s (NAAQS ) hav e bee nattained .
Th e purpos e o f thi s repor t i s t o examin e dat a reliabilit y a s a functio no f dat a completenes s inorde r t o assis t th e EPA init s review

o f th e adequac y andappropriatenes s o f th e curren t NAD B dat a validit y criteria .
We conside r two importan t case s a t opposit e end s o f th e spectru m o f dat a usage : (1 ) th e determinatio no f NAAQ S attainmen t a t a site , whic hi s essentia l t o th e implementatio no f th e Clea nAi r Ac t andwhic hdepend s heavil y oncomplet e data , and(2 ) th e estimatio no f th e statu s of , and
831t6 r 1 x vP.1 8

trend s in , ai r qualit ynationwide , fo r whic hmuc hles s complet e dat a ca n b e tolerate d sinc e dat a ar e poole d fro ma larg e numbe r o f sites
.

METHODOLOG Y

W e tak e th e vie wi n thi s repor t tha t summar ystatistic s base d o n incom

plet e dat a ar e estimate s o f th e summar ystatistic s tha t woul d hav e bee n obtaine d fro mcomplet e data . Moreover , w e trea t th e se t o f observation s a t a sit e a s a rando msampl e fro mth e entir e se t o f pollutan t concentra
tions , eve n i n th e cas e o f regula r bu t intermitten t samplin g (s o tha t a samplin g schedul e o f onc e every si xday s produce s a t mos t 1 7 percen t completeness) .
Fro mthi s viewpoint , th e correc t NAAQ S attainmen t statu s o f a sit e depend s solel yo n th e complet e se t o f measure d pollutan t concentrations , s o tha t misciassificatio n i s possibl e onl yfo r thos e site s havin g incomplet e data . Similarly , th e accurac yo f estimate d trend s i n nationa l ai r qualit y depend s on th e dat a completenes s a t eac hsit e a s wel l a s th e se t o f site s use d t o formth e estimates
. W e hav e thu s se t asid e othe r importan t component s o f dat a reliability , i.e. , measuremen t erro r (du e t o a variet y o f inheren t limitations , suc ha s instrumen t calibratio n and maintenanc e o r recordin g and reportin g practices ) o r th e degre e o f spatia l representa
tivenes s o f measure d concentrations
.
W e adop t tw o form s o f analysis : (1 ) w e analyz e hypothetica l "most
sensitive " scenario s (i n whic hsite s barel yattai n o r fai l t o attai n short-ter m

ai r qualit ystandards , o r wher e th e hypothetica l hig hvaria

bilit yo f pollutan t concentration s make s th e estimatio n o f long-ter m average s mor e difficult ) and (2 ) w e estimat e actua l erro r rate s base d o n 198 1 NAD B dat a (describe d i n Appendixe s Athroug hF) .
Th e criterio n w e us e t o evaluat e th e effec t o f dat a completenes s require

ment s o n th e estimatio n o f nationa l ai r qualit ytrend s i s th e varianc e o f th e estimate d nationa l averag e i n a singl e year . W e sho wtha t stric t dat a
83m6 r l xv iP.1 9

completenes s requirement s affec tth e varianc e i n two distinc tways . Tha t i s , excludin g site s wit h les s complet e dat a has two effects : (1 ) a n increas e i n th e reliabilit yo fthos e site-specifi cestimate s include d i n th e nationa l estimat e an d (2 ) a reductio n i n th e numbe ro fsite s include d i n th e nationa l estimate

. Usin g 198 1 NAD B dat a w

e evaluat e thes e com petin g effect s o n th e varianc e o fth e estimate d nationa l average .

MAJO R FINDING S
I n general , reliabl e determinatio n o fth e short-ter m NAAQ S attainmen t statu s o fa sit e require s more complet e dat a tha n th e othe rdat a use s w e consider

. However , as severa l investigator s hav e noted , increasin g th e dat a completenes s proportio n (u p t o a point ) als o increase s th e chanc e o f misclassifyin g a sit e that , i n fact , has on e exceedance

. Thi s peculiarit y i s du e t o th e fac ttha tth e more complet e th e data , th e greate rth e chanc e o fincludin g th e exceedanc e i n th e sampl e an d thu s derivin g an-expecte d exceedanc e rat e greate rtha n unit ywhe n correctin g fo rth e incompletenes s o fth e dat a record

. Thi s scenari o ma ybe guarde d agains tb yrequirin g essentiall ycomplet e dat a (mor e tha n 95 percen tcompleteness) , o rby modifyin g th e procedur e fo rdeterminin g NAAQ S attainmen tstatus
. W e use d 198 1 dat a i n tw o ways t o analyz e dat a completenes s needs : (1 ) w e estimate d th e expecte d rat e o fmisclassificatio n fo rsite s tha tme t curren tan d alternativ e completenes s requirement s i n 1981 , an d (2 ) fo r long-ter m lea d an d particulat e standard s w e estimate d "critica l intervals " abou tth e standard s withi n whic h misclassificatio n probabilitie s excee d 1 0 percent , an d w

e sho who wthes e interval s narro was more complet e dat a ar e required

. (Critica l interval s ar e define d i n Sectio n 3 , an d shoul d no tb e confuse d wit h confidenc e intervals. )
Eac h for m o fanalysi s serve s a differen tpurpose . Th e firs tindicate s rate s o fmisclassificatio n unde rcurren tcompletenes s requirement s bu ti s les s usefu l fo restimatin g th e consequence s o ftightenin g o rrelaxin g thes e requirements

. Th e secon d for m o fanalysi s use s characteristic s o f 83ii+6 ri xvi iP.2 0



### th e 198 1 dat a t o estimat e th e consequence s o f variou s dat a completenes s requirement s o

nth e classificatio no f leado r particulat e monitorin gsite s whos e long-term

average s ar e nea r th e standard . Th e secondfor m thu s focuse s o

nthos e site s whos e long-term averag e concentration s ar e nea r th e standar dwithou t addressin gth e questio no f ho w many suc hsite s ther e ar e likel y t o b e (whichcanno t b e don e reliabl y i nth e absenc e o f muc hmor e dat a o r stron gassumption s o f uncertai nvalidity) . Th e probabilit y

o f misclassifyin ga nattainmen t sit e unde r curren t dat a completenes s requirement s i s 1 percen t fo r th e 1-hou r C Ostandard , 2 percen t fo r th e 8-hour CO , 24-hou r S0 2 

### , an d24-hou r TS P standards , an d 8 percen t fo r th e dail y maximu m ozon e standar d(se e Tabl e 3-2) . Wheneve r reliabl e estimatio nwa s possible , w e estimate dth e probabilit y o f mis classifyin ga nonattainmen t site . Fo r th e 24-hou r SOgstandar dan dth e 1-hour COstandard , reliabl e estimatio nwa s no t possibl e du e t o th e smal l numbe r o f nonattainmen t site s (les s tha n10) . Th e probabilit y i s les s tha n1 percen t fo r th e 8-hou r C Ostandar dan dth e dail y

maximum ozon e standard , an

d6 percen t fo r th e 24-hou r TS P standard .
W e attempte dt o us e th e firs t for m o f analysi s t o estimat e th e effect s o f alternativ e dat e completenes s requirement s onth e probabilit y o f misjudg in gth e attainmen t o f short-term standards . Thi s wa s don e b y restrictin g th e analysi s t o thos e site s meetin gth e alternativ e requirements . Fo r continuousl y

monitore dpollutants , a relaxatio no f th e 7 5 percen t require men t t o 5 0 percen t produce dn o appreciabl e differenc e i nth e probabilit y o f misclassification . It i s theoreticall y possible , bu t impractical , t o reduc e th e probabilit y o f a fals e determinatio no f nonattainmen t t o zer o b y requirin g9 5 percen t complet e data . Fo r th e curren t 24-hou r TS P stan dard , w e examine dsite s meetin gcompletenes s requirement s o f 5 , 12 , an d 2 5 percent , i.e. , requirement s o f 5 , 11 , an d2 3 vali dsample s pe r quarter , respectively . Again , th e analysi s wa s insensitiv e t o difference s indat a completeness , althoug hth e estimat e fo r 5 an d2 5 percen t completenes s differe dslightly .

83146 T i xvii iP.2 1

Fo rlong-ter m standards , th e 198 1 dat a indicat e tha tth e probabilit yo f misclassifyin g th e statu s o fa sit e tha thas actuall yattaine d a standar d an d tha tmeet s curren tNAD B completenes s requirement s i s 2 percen tfo rTSP an d les s tha n 1 percen tfo rothe rpollutant s (se e Tabl e 3-1)

. Th e chanc e o fmisclassifyin g th e statu s o fa TSP monitorin g sit e tha thas no t attaine d th e annua l standar d i s estimate d t o be 9 percent
. Th e estimate d chanc e o fmisclassificatio n i s greate rfo rTSP site s tha thav e no t attaine d th e standar d tha n fo rthos e tha thav e attaine d th e standar d becaus e th e nonattainmen tsite s ten d t o be onl yslightl yabov e th e standard , wherea s th e attainmen tsite s ten d t o be wel l belo wth e stan dard

. Fo rothe rlong-ter m standards , ther e ar e to o fe wnonattainmen t site s t o reliabl yestimat e th e rat e o fmisclassifyin g suc h sites

. Thes e result s sugges ttha tmisclassification s ar e unlikel yunde rcurren tdat a completenes s requirements

. Thi s i s tru e provide d tha tth e 198 1 dat a ar e representativ e o fothe ryears , i.e. , s o tha tactua l long-ter m average s ar e wel l belo wth e standar d fo ral l bu ta smal l fractio n o fsites

. A s i n th e cas e o fshort-ter m standards , th e misclassificatio n rat e wa s approximatel yth e same fo rsite s meetin g variou s dat a completenes s requirements

. Th e exceptio n i s tha tth e estimate d rat e o fmisclassi
ficatio n o fTSP site s tha thav e no tattaine d th e standar d drop s fro m 9 percen tt o 3 percen twhe n 2 5 percen tcompletenes s i s required

. Thus , direc testimatio n o fexpecte d rate s o fmisclassificatio n generall y require s more dat a o rstronge rassumption s (o funcertai n validity ) t o distinguis h th e effect s o falternativ e dat a completenes s requirements

. Usin g th e secon d for m o fanalysis , i n whic h w e estimate d "critica l intervals " abou ta give n standard , w e analyze d th e effec to fdifferen t completenes s requirement s o n th e accurac yo fclassification s o fattainmen t o fth e quarterl ylea d standar d an d th e annua l TSP standard

. W e gav e specia l attentio n t o thes e two pollutant s sinc e the yar e typicall ysample d onl yonc e ever ysi xdays

. Fo reac h pollutan tw e estimate d th e expecte d tota l numbe ro fmisclassification s (tha tis , misclassification s o fbot h

83i"+6 rI xi xP.2 2

attainmen t an

dnonattainment ) t o b e les s tha n5 percent ; th e estimate d percentag e fo r leadwa s les s tha ntha t fo r TS P (se e Figure s 3- 1 throug h 3-3) . Fo r lead(quarterl y standar d= 1. 5 pg/m 3 ) , th e estimate dcritica l interva l whe n1 2 sample s pe r quarte r ar e require di s -1 7 percen t t o +2 7 percent , i.e. , 1 7 percen t below t o 2 7 percen t abov e th e standard . Fo r TS P (annua l standar d= 7 5 ug/m 3 ) , th e critica l interva l whe nfiv e sample s pe r quarte r ar e require di s -1 1 percen t t o +1 3 percent . I f 1 2 TS P sample s pe r quarte r wer e required , th e interva l woul dnarrow t o - 7 percen t t o + 8 percent .
I f our goa l wer e t o contai nthes e critica l interval s withi n± 1 0 percen t o f th e respectiv e standards , the nstrengthene drequirement s shoul db e considere dfo r lea dandpossibl y fo r particulates . Indeed , judgin gfrom ou r analysi s o f TSP , thi s goa l woul dbe achieve dunde r a recentl y propose d revisio nt o standard s fo r particulat e matte r tha t include s a requiremen t o f 1 2 vali dobservation s pe r quarte r (EPA , 1984 ; 4 9 F R10434) .
A simila r critica l interva l analysi s o f short-term standard s i s possible , althoug hthi s woul drequir e additiona l assumption s abou t th e frequenc y distributio no f concentration s a t individua l sites . I nth e interest s o f time an dprojec t resource s we di dno t conduc t thi s analysis , althoug hi t may be usefu l t o d o s o inth e future .
Fo r reliabl e estimatio no f nationa l trends , th e benefit s o f relaxin gdat a completenes s requirements , i.e. , broadenin gth e bas e o f site s fro m whic h t o form th e nationa l averag e ove r successiv e years , outweig hth e dis advantage s o f includin gth e site s tha t hav e les s complet e dat a and therefor e les s reliabl e summary statistic s (se e Sectio n5) . Thi s i s becaus e th e variabilit y inai r qualit y

amon gsite s outweigh s th e samplin g variabilit y

a t individua l sites . Althoug hno optimu m requiremen t canb e identifie dfro

m th e analysis , th e curren t 5 0 percen t dat a completenes s requiremen t use dfo r th e annua l repor t onnationa l ai r qualit y trend s coul db e relaxe dwithou t increasin gcalculate dvariances . Thi s conclusio n i s base donth e distributio no f th e percentage s o f dat a completenes s found

83146 T i xxP.2 3

inth e NADB record s fo r 1981 , an dassume s tha t th e distributio nwoul dno t chang e substantiall y infutur e year s i f th e curren t requiremen t fo r estimatin gnationa l trend s wer e relaxed .

RELATE DISSUE S

We hav e attempte dt o b e thoroug hwithi nth e scop e o f thi s study , howeve r we mus t poin t ou t tha t dat a completenes s requirement s nee dt o b e evaluate d i na broade r contex t tha t include s
(1 ) overal l dat a quality ;
(2 ) th e procedure s use dt o determin e th e attainmen t statu s o f a site , an

d
(3 ) th e ambien t standard s themselves .
Some alternative s t o th e curren t procedur e fo r determinin gshort-term attainmen t statu s us e a greate r numbe r o f th e highconcentration s recorde d at a sit e (se e Sectio n2) . Althoug hthes e alternativ e procedure s ar e somewha t mor e complicated , the y appea r t o b e les s sensitiv e t o incomplet e dat a an davoi dth e counter-intuitiv e result , note dabove , tha t th e chanc e o f misclassificatio nincrease s wit hincreasin gdat a completeness . Althoug hthi s repor t focuse s o ndat a completeness , ther e ar e othe r aspect s o f dat a reliability , e.g. , qualit y assuranc e procedures . I t i s importan t t o remembe r tha t requirin gmor e complet e data , e.g. , requirin gmor e frequen t intermitten t sampling , i s no t th e onl y way t o increas e dat a reliability , an

dno t necessaril y

th e mos t cost-effectiv e way . Thu s al l aspect s mus t be considere dtogethe r i f enhance ddat a reliabilit y i s th e objective . Similarly , th e questio no f dat a reliabilit y belong s t o a large r circl e o f issue s concernin geffectiv e ai r qualit y management . I f increase ddat a
83it6 r i xx iP.2 4



### reliabilit y

reduce s th e chanc e o f misclassification , the nth e margina l cost s o f improve ddat a reliabilit y shoul db e weighe dagains t th e economi c an dpubli c healt hbenefit s o f fewe r misclassifications . I f we reduc e th e chanc e tha t anattainmen t sit e wil l b e misclassifie da s nonattainment , the nunnecessar y

administrativ e an

dcontro l cost s wil l als o b e reduced . Likewise , i f we reduc e th e chanc e tha t a nonattainmen t sit e wil l b e misclassifie da s attainment , the npresumabl y publi c healt hi s bette r maintained . I

nevaluatin gth e latte r benefit , however , on e shoul dbea r i n mindtha t a margi no f safet y i s incorporate dint o ambien t standard s s o tha t actua l pollutan t concentration s a t a nonattainmen t sit e may ye t b e below level s causin gadvers e healt heffects . Moreover , i t i s possibl e tha t wit hsufficien t effor t devote dt o dat a reliability , our certaint y abou t actua l ai r qualit y coul dexcee dou r certaint y abou t ai r qualit y healt heffects .
I nshort , monitorin grequirement s (dat a completeness , qualit y assurance , number s o f monitors , frequenc y o f sampling , etc. ) an dothe r regulator y issues , suc ha s th e margi no f safet y embodie di nambien t standards , ar e interdependent . Fo r example , th e smalle r th e margi no f safet y use di n derivin ganai r qualit y standard , th e les s w e canaffor dt o misjudg e th e attainmen t statu s o f a site , an dth e greate r th e nee dfo r highl y reliabl e data .

RECOMMENDATION S
On e us e o f th e NADB dat a tha t we conside r i s th e estimatio no f th e statu s o f an dtrend s i nai r qualit y nationwide . Tightenin gth e dat a requirement s ha s tw o competin geffects : (1 ) th e site s meetin gth e stricte r require ment s (an

dtherefor e include dinth e nationa l average ) hav e mor e reliabl e ai r qualit y

summary statistics , bu t (2 ) th e exclusio no f site s wit hles s reliabl e statistic s reduce s th e sampl e siz e use dt o for m th e nationa l average . Ou r conclusio ni s tha t th e curren t 5 0 percen t dat a completenes s requiremen t fo r determinin gnationa l trends , whic hi s a relaxatio no f th e 7 5 percen t NADB requirement , shoul dbe furthe r relaxe dbecaus e th e

ssme r l xxi iP.2 5

advantag e o f a larg e sampl e siz e outweigh s th e advantag e o f reliabl e statistic s a t individua l sites . Unfortunately , th e optimu m requiremen t canno t b e identifie dfro m ou r analysis , s o tha t an y specifi c recommenda tio nwil l requir e furthe r consideration .
Th e EPA staf f responsibl e fo r th e estimatio nan dreportin go f nationa l ai r qualit y trend s hav e direc t acces s t o NADB data . Thi s i s a nexampl e o f a specia l grou po f NADB user s wh o ca nappl y dat a completenes s requirement s accordin gt o thei r particula r needs . I nconsiderin gth e releas e o f NAD B dat a t o th e genera l user , however , i t appear s wis e t o continu e th e polic y o f usin gon e se t o f dat a completenes s requirements . Specia l user s wit h direc t acces s t o NADB data , suc ha s thos e responsibl e fo r th e estimatio n o f nationa l ai r qualit y trends , shoul dcontinu e t o determin e th e degre e o f dat a completenes s neede dfo r thei r particula r application .
Becaus e o f th e practicalit y o f usingon e se t o f requirements , th e require ment s shoul db e base do nth e commo napplicatio nhavin gth e greates t nee d fo r complet e data . O nthi s basis , we hav e focuse donth e issu e o f whethe r curren t NADB criteri a provid e sufficien t protectio nagains t misclassifyin g th e attainmen t statu s o f a sit e wit hrespec t t o short-term o r long-term standards . Certainly , accurat e estimate s o f nationa l trend s requir e les s complet e dat a tha nd o accurat e classification s o f individua l site s (compar e Figure s 5- 1 throug h5- 9 wit hTable s 3- 1 and3-2) . O f course , we canno t compar e thes e dat a need s quantitativel y unti l w e prescrib e th e accuracie s accept abl e i neac hcase , bu t th e evidenc e supportin gth e statemen t i s clea r nevertheless . It i s als o clea r tha t ther e ar e othe r possibl e dat a uses , no t examine di nthi s report , tha t dependeve nmor e heavil y o ncomplet e dat a tha ndoe s th e determinatio no f attainmen t status , e.g. , inferrin gth e maximum 10-minut e concentratio nfro m hourl y data . But , fo r th e purpos e o f establishin gappropriat e NAD B requirement s fo r complet e data , th e determinatio no f attainmen t statu s seem s t o b e th e mos t exactin gus e o f ai r qualit y

dat a tha t i s commo nt o al l th e pollutant s fo r whic hwe hav e nationa l ambien t ai r qualit y standards .
83iJ+6 r l xxii iP.2 6

Direc t estimate s o f th e expected misclassificatio n rat e fo r continuousl y monitore d pollutant s d o no t chang e appreciabl ywhe n th e curren t NADB requiremen t o f 7 5 percen t dat a completenes s i s relaxed t o 5 0 percent . Analyse s o f th e mos t sensitive scenario s (whichcoul d becom e mor e commo n than the

ywer e in 1981 ) warn agains t thi s relaxation , however . I n particular , i f the procedure s fo r determining the attainmen t o f short-ter m standard s remai n unchanged , i t seems bes t fo r th e time bein g t o als o maintai n curren t dat a completenes s requirement s fo r continuousl ymonitore d pollutants . Similarly , in th e cas e o f intermitten t sampling , e.g. , fo r lea d an d particulat e matter , direc t estimate s o f misclassificatio n rate s barel y distinguis hbetwee n th e alternative dat a completenes s requirement s we consider
. I

n general , the NAD

B dat a validitycriteri a requir e onlyfive sample s pe r quarter , an d thu s only2 0 sample s pe r year . Fo r lead , however , 1 2 sample s pe r quarte r ar e required fo r attainmen t determination s (EPA , 1979 ; 4 3 F

R 46266) . I

n eithe r case , i t i s muchmor e difficul t t o ensur e the correc t classificatio n o f ai r qualit ystatu s unde r a n inter

mitten t sampling
. Critica l interva l analyse s (Figur e 3- 1 throug h3-3 ) demonstrat e th e advantage s o f strengthening dat a completenes s requirement s fo r lea d an d particulates
. I t i s therefor e advisabl e t o adop t the recentl ypropose d requiremen t o f 1 2 particulat e sample s pe r quarte r (EPA , 1984 ; 4 9 F

R 10434 ) an

d t o conside r even mor e frequen t sampling o f lead .
Concerning th e issue s related t o dat a completeness—les s centra l t o th e scop e o f thi s stud

ybu t no les s importan t fo r effective ai r quality management—w e recommen d consideratio n o f two change s in procedures . (1 ) Agreate r numbe r o f th e hig hconcentration s recorded a t a sit e shoul d be used in orde r t o provid e mor e robus t short-ter m attainmen t inferences
.

83m6 T i XXI VP.2 7



### (2 ) Dat a completenes s consideration s shoul db e incorporate di nth e review , revision , andinterpretatio no f ai r qualit y standards , alon gwit hsuc hrelate dissue s a s th e cos t o f mor e complet e data , othe r limitation s o ndat a reliability , th e cos t o f misclassifyin gattainmen t status , an dth e existin gmargin s o f safet y use dt o se t threshold s fo r ambien t concentrations .

831t6 r 1 

## xx vP.2 8

1 INTRODUCTIO N

The U.S . Environmenta l Protectio nAgenc y (EPA ) store s ambien t ai r qualit y data , obtaine dfrom a nationwid e networ ko f monitorin gstations , i nth e Nationa l Aerometri c Dat a Bank(NADB) . Statistic s summarizin gth e ai r qualit y at eac hmonitorin gstatio nar e als o store di nth e NADB . Thes e summar y statistic s ar e mad e availabl e t o th e governmen t andpubli c provide dtha t certai ndat a validit y criteri a ar e met , chie f amon gthes e bein grequirement s fo r dat a completenes s (give ni nAppendi xG) . Thi s dat a screenin gpolic y help s t o ensur e tha t NAD B summar y statistic s ar e reliable . Moreover , th e polic y o f usin gon e se t o f dat a validit y criteri a regardles s o f th e intende dus e o f th e dat a i s necessar y sinc e th e EPA mus t respondquickl y t o numerou s request s fo r NAD B data . I t i s possible , however , tha t some informatio nmay b e unnecessaril y

los t t o th e genera l NAD B use r becaus e o f th e stringenc y o f th e dat a validit y criteria . Nevertheless , a stringen t criteri a ma y b e neede dfo r some purposes , notably , fo r determinin gwhethe r Nationa l Ambien t Ai r Qualit y Standard s (NAAQS ) hav e bee nattained .
The purpos e o f thi s repor t i s t o examin e dat a reliabilit y a s a functio no f dat a completenes s i norde r t o assis t th e EPA i nit s review

o f th e adequac y andappropriatenes s o f th e curren t NAD B dat a validit y criteria .

BACKGROUN D
Ai r qualit y

dat a ar e provide dt o th e Nationa l Aerometri c Dat a Bank(NADB ) primaril y

b y stat e an

dloca l agencies . Include di nth e NAD B recor do f ai r qualit y summar y

statistic s fo r a monitorin gstatio ni s a "criteri a flag "


### 83m6 r 2 

### 1P.2 9

indicatin gwhethe r NAD B dat a validit y criteri a ar e met . Thes e (an dother ) dat a validit y

criteri a ar e presente dinAppendi xG , bu t the y may be roughl y summarize da s a 7 5 percen t dat a completenes s requirement . (Not e tha t th e monitorin gschedul e fo r lea dan dparticulat e matte r i s onc e ever y si xdays . Inthes e case s o f intermitten t samplin go f 24-hou r concentra tions , NAD B criteri a requir e fiv e sample s pe r quarte r an dthu s 2 0 sample s pe r year .
Inrespondin gt o typica l request s fo r NAD B data , th e recor do f a sit e failin gt o mee t th e criteri a may be writte nwit hquestio nmark s t o indicat e questionabl e dat a reliability , o r th e recor dmay no t be writte n a t all . Thus , wherea s authorize duser s o f NAD B dat a hav e acces s t o al l records , th e record s indicatin gtha t NAD B criteri a hav e bee nme t consti tut e a commo nsubse t o f NADB dat a tha t i s reliabl e an dwidel y

available .
Inthi s repor t we conside r th e effec t o f excludin gai r qualit y dat a fro

m site s tha t fai l t o mee t certai ndat a completenes s requirements . Mor e specifically , w e conside r whethe r curren t NAD B requirement s appea r t o be satisfactor y

o r nee dt o be changed , and , i nth e latte r case , whethe r dat a completenes s requirement s shoul dvary wit hdat a usag e andtyp e o f monitor ing(intermitten t o r continuous) .
W e conside r two importan t case s a t opposit e end s o f th e spectru m o f dat a usage : (1 ) th e determinatio no f NAAQ S attainmen t at a site , whic hi s essentia l t o th e implementatio no f th e Clea nAi r Ac t an dwhic hmake s a heav y deman dfo r dat a completeness , an d(2 ) th e estimatio no f th e statu s o f an dtrend s i nai r qualit y nationwide , fo r whic hmuc hles s complet e dat a canbe tolerate d(sinc e dat a ar e poole dfro m a larg e numbe r o f sites) .
Fo r eac ho f thes e two dat a use s we conside r summary statistic s relevan t t o short-term

an dlong-term

ai r qualit y

standards . Tha t is , we conside r th e numbe r o f exceedance s o f th e short-ter m standard s (havin ganaveragin g tim e o f 2 4 hour s o r less) , an dth e annua l o r maximum quarterl y

average . Again , b

y treatin gbot hpea kvalu e summar y statistic s (wher e th e effec t o f

8311+ 6 2P.3 0

incomplet e dat a i s pronounced ) an dlong-term

average s (wher e dat a com pletenes s need s ar e minimal) , we hop e t o provid e usefu l benchmark s fo r othe r dat a uses . Currently , th e attainmen t o f mos t nationa l ambien t ai r qualit y standard s i s determine dseparatel y fo r eac hyea r o f data . However , a three-yea r attainmen t perio di s use di nth e cas e o f ozone , andmulti-yea r period s ar e beingconsidere dfo r othe r short-term standards . We therefor e addres s th e dat a completenes s neede dfo r bot hsingle-yea r andmulti-yea r attainmen t standards .

METHODOLOG YANDORGANIZATIO N OF RESULT S

We tak e th e view

i nthi s repor t tha t summary statistic s base donincom plet e dat a ar e estimate s o f th e summary statistic s tha t woul dhav e bee n obtaine dfrom

complet e data . Moreoever , w e trea t th e se t o f observation s a t a sit e a s a rando m sampl e fro

m th e entir e se t o f pollutan t concentra tions , eve ninth e cas e o f regula r but intermitten t samplin g(s o tha t a samplin gschedul e o f onc e every si xday s produce s at mos t 1 7 percen t completeness) .
From thi s viewpoint , th e correc t NAAQ S attainmen t statu s o f a sit e depend s solel y o nth e complet e se t o f measure dpollutan t concentrations , s o tha t misclassificatio ni s possibl e onl y fo r thos e sites havin gincomplet e data . Similarly , th e accurac y o f estimate dtrend s i nnationa l ai r qualit y depend s o

nth e dat a completenes s a t eac hsit e a s wel l a s th e sampl e o f site s usedt o for

m th e estimates . We hav e thu s se t asid e othe r importan t component s o f dat a reliability , i.e. , measuremen t erro r (due t o a variet y o f inheren t limitations , sucha s instrumen t calibratio nan dmaintenanc e o r recordin gandreportin gpractices ) o r th e degre e o f spatia l representa tivenes s o f measure dconcentrations .
We adop t two form s o f analysis . I nSectio n2 we analyz e hypothetica l "most-sensitive " scenario s (inwhic hsite s barel y attai no r fai l t o attai n
8314 6 2 3P.3 1

short-term

ai r qualit y

standards , o r inwhic hth e hypothetica l high variabilit y

o f pollutan t concentration s make s th e estimatio no f long-term average s mor e difficult) . I nSectio n3 we asses s th e likel y erro r rate s base do n198 1 NADB dat a (describe dinAppendixe s A throug hF) .
I nou r treatmen t o f short-term NAAQS , th e assigne dstatu s o f a sit e i s base do na quantit y

know na s th e "expecte dnumbe r o f exceedances, " tha t is , attainmen t statu s i s determine dassumin gtha t th e rati o o f observe d exceedance s t o tota l observation s i s maintaine dfo r th e complet e dat a set .
Th e criterio nwe us e t o judg e th e degre e o f dat a completenes s require dt o accuratel y

determin e NAAQ S attainmen t i s th e chanc e o f misclassifyin gth e statu s o f a site . Th e chanc e i s zer o fo r sites havin g10 0 percen t complet e dat a or fo r site s havin gno exceedances . Otherwis e th e chanc e o f misclassificatio ndepend s o nbot hth e degre e o f dat a completenes s an dth e actua l numbe r o f exceedances .
I nSectio n2 we conside r th e effec t o f dat a completenes s o nth e chanc e o f erroneousl y

classifyin gth e statu s o f a sit e a s nonattainmen t whe ninfac t th e referenc e concentratio nha s bee nexceede dexactl y onc e i nth e cours e o f th e year . We als o conside r th e chanc e o f erroneousl y classifyin gth e statu s a s attainmen t whe nth e sit e ha s exactl y tw o exceedances . Thi s analysi s i s als o carrie dou t fo r ai r qualit y standard s allowin gno mor e tha nthre e exceedance s inthre e years .
I nSectio n3 we us e 198 1 NADB dat a t o estimat e th e actua l (but unknown ) rates o f misclassification . W e firs t estimat e th e frequenc y distributio n amongsite s o f th e actua l numbe r o f exceedances . We the nus e thi s distributio nt o estimat e th e probabilit y o f misclassifyin gsite s meetin g variou s dat a completenes s requirements .
Fo r long-term

(annua l o r quarterly ) ambien t standards , th e chanc e o f misclassifyin ga sit e depend s no t onl y o nth e degre e o f dat a completenes s a t th e sit e bu t als o o nth e empirica l frequenc y distributio no f th e entir e yea r o f pollutan t concentration s a t th e site . I nSectio n2 we asses s dat a
8314 6 2 4P.3 2



### completenes s requirement s b y assumin gtha t th e actua l averag e concentra tio nat th e sit e i s 1 0 percen t abov e o r below th e standard , an

dtha t th e standar ddeviatio no f th e yea r o f concentration s a t th e sit e i s twic e th e average . I

nSectio n3 we empiricall y estimat e probabilitie s o f misclassi ficatio nusin g198 1 data .
I nSectio n4 we introduc e a criterio nfo r evaluatin gth e effec t o f dat a completenes s requirement s onth e estimatio no f nationa l ai r qualit y trends , namel y

th e varianc e o f th e estimate dnationa l averag e ina singl e year . Excludin gsite s wit hles s complet e dat a ha s two effects : (1 ) th e reliabilit y

o f th e site-specifi c estimate s include dinth e nationa l estimat e i s increased , an d(2 ) th e numbe r o f site s include dinth e nationa l estimat e i s reduced . InSectio n5 , using198 1 NADB data , we evaluat e thes e competin geffect s o nth e varianc e o f the estimate dnationa l average .

8314 6 2P.3 3



### STATISTICAL PROCEDURE S FO RDETERMINING TH E AI RQUALIT YSTATU S O F A SIT E

ANNUAL O

RQUARTERL Y(LONG-TERM ) AVERAGE S
Suppos e we wis ht o kno w (u pt o measuremen t error ) th e annua l averag e concentratio ny ^ o f a pollutan t a t a particula r site , wit hinde xi , base d ona complet e se t o f N measurements , bu t tha t onl y n^ vali dmeasurement s wer e recorded , th e remainin gm^ = N - n ^ beingeithe r invali do r missing . Lettin gp ^ denot e th e proportion , n^/N , o f dat a completeness , i t canbe A 

### show n (Cochran , 1963) tha tth e sampl e averag e u . ha s expecte d valu e p H 

### an d 2 varianc e ( 1 - p i 

### )/p i 

### (o^N) :

A E ji i = u i

1 

### " p 

### i ° i

Moreover , annua l ai r qualit y measurement s usuall y entai l larg e number s o f bot hmissin gan

dobserve dvalue s (m

^ an dn^ ) wit hrelativel y few extrem e values . Unde r thes e conditions , formulate dmor e precisel y b y Cochra n A 

### (1963) , wh o cite s Haje k(1960) , th e samplin gdistributio no f y , i s approximatel y

normal . Thu s [( 1 - p ^ )/p-j]o i 

### / N i s a natura l measur e o f th e erro r i nestimatin gth e annua l averag e (\ij)

o f al l N

measurement s b

y th e averag e o f a proportio n (p.j ) o f th e N

measurements .

83146 T 3P.3 4



### W e may us e thes e results—th e expecte dvalue , variance , an dapproximat e A 

### normalit y

o f u^—t o determin e th e minimu m acceptabl e proportio no f measurements , pg , provide dtha t (1 ) we specif y a relativ e erro r tolerance , e , suchtha t error s o f large r magnitud e have , say , onl y a 5 percen t o f occurring , an

d(2 ) we hav e some ide a o f th e coefficien t o f variation , Oi/\i-. Specificall y

w e have
1 ^ - u. | P r I — — > e ) = P r eu .

(Varu\) 1 / 2 

### (Var P 

### . ) 1 / 2



### = 2 1 - $ eu .

.(Varuj ) 1/

2

1 - $ e • —

v< (NPi ) l/2\- |

. 1 - p . 1 H


### wher e $ i s the standar dnorma l cumulativ e distributio nfunction . The abov e expressio nmay

be use dt o calculat e eithe r p gfro m a prescribe d probabilit y

(e.g. , 5 percent) , or a confidenc e boundo f a prescribe dleve l (e.g. , 9 5 percent ) o

nth e absolut e relative erro r (|u . - u.|/u- ) giventhe proportio no f dat a completenes s (p^ ) an dth e coefficien t o f variatio n(0^/u.j ) a t a particula r site . Tabl e 2- 1 show s th e minimu m proportio np

gcorrespondin gt o severa l combination s o f e , o-j/u-j , an dN . Tabl e 2- 2 show s e a s a functio no f N , a*/\ii»an

dp. . Not e tha t th e 7 5 percen t criterio nusedb y th e SAROAD system implie s tha t partia l annua l average s ar e no t likel y t o diffe r b

y mor e thanappproximatel y one percen t fro

m complet e annua l average s i f th e coefficien t o f variatio nequal s unit y (10 0 percent) . Th e correspondin g erro r for th e 5 0 percen t completenes s criterio ncommonl y usedinreportin g trend s inannua l average s i s approximatel y tw o percen t (for a

nindividua l site) .

8314 6 3P.3 5

TABL E 2-1 . Th e require ddat a completenes s proportio n(pg ) a s a functio no f th e tota l numbe r o f possibl e measurement s (N) , th e coefficien t o f varia tion(ai/y-j) , an

dth e prescribe dmaximu m relativ e erro r (e ) occurrin g wit h9 5 percen t probability .

N= 876 0 (hrs/yr )

N= 292 0 ( 3 hrs/yr )

N= 109 5 ( 8 hrs/yr )

N= 36 5 (days/yr )

N= 9 1 (days/qtr )

N= 3 0 (days/mo ) a i /y i

0. 5 1. 0 2. 0
0. 5 1. 0 2. 0
0. 5 1. 0 2. 0
0. 5 1. 0 2. 0
0. 5 1. 0 2. 0
0. 5 1. 0 2. 0 .00 1 0.9909 6 0.9977 2 0.9994 3
0.9969 7 0.9992 4 0.9998 1
0.9988 6 0.9997 2 0.9999 3
0.9996 2 0.9999 1 0.9999 8
0.9999 1 0.9999 8 0.9999 9
0.9999 7 0.9999 9 1.0000 0 .0 1

0.5229 7 0.8143 1 0.9460 7
0.7668 4 0.9293 6 0.9813 5
0.8976 5 0.9722 9 0.9929 2
0.9633 9 0.9905 9 0.9976 3
0.9906 1 0.9976 4 0.9994 1
0.9968 9 0.9992 2 0.9998 0 e .0 5

0.0420 1 0.1492 3 0.4123 3
0.1162 6 0.3447 9 0.6779 3
0.2597 1 0.5839 0 0.8487 8
0.5127 8 0.8080 5 0.9439 4
0.8084 8 0.9440 9 0.9854 1
0.9275 6 0.9808 5 0.9951 4 .1 0

0.0108 4 0.0420 1 0.1492 3
0.0318 4 0.1162 6 0.3447 9
0.0806 3 0.2597 1 0.5839 0
0.2083 1 0.5127 8 0.8080 5
0.5134 6 0.8084 8 0.9440 9
0.7619 7 0.9275 6 0.9808 5 1.0 0

0.0001 1 0.0004 4 0.0017 5
0.0003 3 0.0013 1 0.0052 3
0.0008 8 0.0035 0 0.0138 4
0.0026 2 0.0104 1 0.0404 0
0.0104 4 0.0405 0 0.1444 6
0.0310 2 0.1135 1 0.3387 1

8314 6 8313 2P.3 6

TABL E 2-2 . Relative erro r ( E ) fo r dat a completenes s proportion s p . = 0.5 0 an d p i = 0.75 .

N= 876 0 (hrs/yr )

N= 292 0 ( 3 hrs/yr )

N= 109 5 ( 8 hrs/yr )

N= 36 5 (days/yr )

N= 9 1 (days/qtr )

N= 3 0 (days/mo ) cyy .

0. 5 1. 0 2. 0
0. 5 1. 0 2. 0
0. 5 1. 0 2. 0
0. 5 1. 0 2. 0
0. 5 1. 0 2. 0
0. 5 1. 0 2. 0 

### .5 0

0.0104 7 0.0209 4 0.0418 8
0.0181 4 0.0362 7 0.0725 4
0.0296 1 0.0592 3 0.1184 6
0.0512 9 0.1025 9 0.2051 8
0.1027 3 0.2054 6 0.4109 2
0.1789 2 0.3578 4 0.7156 8 

### .7 5

0.0060 5 0.0120 9 0.0241 8
0.0104 7 0.0209 4 0.0418 8
0.0171 0 0.0342 0 0.0683 9
0.0296 1 0.0592 3 0.1184 6
0.0593 1 0.1186 2 0.2372 4
0.1033 0 0.2066 0 0.4132 0

8314 6 8313 2P.3 7

I ninterpretin gth e minimu m proportion s give ninTabl e 2-1 , not e tha t a give nmaximu

m relativ e erro r e may seem eithe r excessivel y stringen t o r to o lenien t dependin gonth e particula r application . Fo r example , 1 0 percen t relativ e erro r may be to o larg e i f we nee dt o determin e th e attainmen t statu s o f a sit e whos e annua l averag e i s typicall y withi n1 0 percen t o f th e annua l averag e ambien t standar dwherea s fo r a sit e whos e annua l averag e i s typicall y les s tha nhal f th e standar d, a 1 0 percen t relativ e erro r specificatio nmay be unnecessaril y

stringent .

SHORT-TERM

CONCENTRATION S
Th e Observe dNumbe r o f Short-Term Exceedance s
Curren t nationa l ambien t ai r qualit y standard s (NAAQS ) fo r pollutan t concentration s average dove r 2 4 hour s o r les s influenc e th e presentatio n an danalysi s o f highshort-term concentrations . Th e short-term standard s fo r mos t pollutant s stipulat e a nambien t concentratio ntha t shoul dno t be exceede dmor e tha nonc e pe r year , whic himplie s tha t th e secondhighes t short-term

concentratio nrecorde da t a sit e determine s whethe r th e sit e i s i nattainmen t o f th e standard .
It i s possibl e t o trea t th e secondhighes t valu e ina year' s partia l sampl e o f short-term concentration s a s anestimat e o f th e secondhighes t concentratio nina complet e sample , jus t a s w e treate dlong-term averages . Thi s turn s ou t t o be a difficul t approac hfro m a statistica l viewpoint , however , becaus e o f th e grea t variabilit y o f th e secondhighes t concentratio nan

dth e influenc e o f th e unknow ntai l distributio no f short term concentration s o

nth e samplin gbehavio r o f th e secondhighes t value .
Another , mor e workabl e approach , at leas t fro m a statistica l perspective , i s t o examin e th e sequenc e o f short-term concentration s at a sit e ove r a one-yea r perio dan

dt o simpl y

coun t th e numbe r o f time s th e ambien t concentratio nexceed s th e standard . I f th e coun t i s no mor e tha none , th e short-term

standar di s attained ; otherwis e i t i s not . 8314 6 3 1 0P.3 8

Inthi s approac hth e sequenc e o f short-term concentration s i s converte dt o a 0- 1 value dsequence , inwhic h1 indicate s tha t th e concentratio nexceed s th e standar dan

d0 indicate s otherwise . Th e key questio nregardin gth e effec t o f dat a completenes s ondeterminin gattainmen t statu s i s whethe r a n exceedanc e ha s bee noverlooked . Mor e specifically , we wis ht o know th e probabilit y

tha t a sit e ha s two o r mor e exceedance s but tha t no mor e tha n on e exceedanc e i s observe dinou r partia l sample . We may formulat e thi s a s th e followin gconditiona l probability :

P r (k i < 1 | X . = 2 )
wher e
k. : = th e numbe r o f exceedance s inth e observe dsampl e o f siz e n^

x i = th e unknow

ntota l numbe r o f exceedance s tha t occurre ddurin gth e year .
Th e reaso nfo r focusin go nth e cas e x . = 2 i s tha t thi s i s th e mos t challengin gproblem fo r a fixe dpartia l sampl e size , n^ ; we woul dd o wel l t o prescrib e th e chance , sa y 3 , we ar e willin gt o tak e tha t we fai l t o observ e th e two exceedances , sinc e fo r large r value s o f x ^ ou r chanc e o f detectin ga violatio nonl y improves . Mor e generally , fo r a standar d allowin gno mor e tha nr exceedance s w e woul dwan t t o calculat e th e probabilit y

tha t r o r fewe r exceedance s ar e observe di nth e partia l sampl e whe nther e ar e r + 1 exceedance s inth e complet e sample . Thi s conditiona l probabilit y i s give nb

y th e hypergeometri c distribution : 

# -"••".•-ft ) t : 

# *•:)/(:,) •
Fo r futur e referenc e we not e tha t

E (k i | X. , Pj ) = p i X i

8314 6 3 

### 1 1P.3 9



## an d N - x . Va r (k . | x. , p ^ = TTT 

### P i 

### ( l - p ^ x i



### (see , fo r example , Johnso nan dKotz , 1969) . Returnin gt o th e chanc e tha t no t al l r + 1 exceedance s ar e detected , we have



## o r

Notingtha t e=Pr(k i 

### <r|x i 

### =r+l )

1 - 3 = Pr (k i 

### = r + 1 | x i 

### = r + 1 )

' N - r - 1 \ // N 

## n, - r - l) /



## In ,

n 

### i n 

### i " 1 n 

### T ' r 

### N N - 1 •* * N

- r

i ~ j



### _" i / N

\ j

- j " N

\H - j/ ~ N

- j

N \ N - j / N

- j

= 

# P i -iHhr ( 1 

# - PI >

8314 6 3 1 2P.4 0



### w e see tha t 

### 1 

# - s = Pi[p , 

# - rh- ( i 

# - oy]

•• •

# h - N-T

T( 1 

# - PI> ]

S 

# P , [p , - i <

1 - p,) ] -[Pi-fi(l-pi> ]

" P i " ( 2 ) l P i ( 1 

### " p 

### i > * • "

+ 

# (- 1 

# ' 1 

# " r ! 

# (i ) p,(i-p,) '
Pf 1 

# * £^ j+1 ) 

# (*) ' Pi" J+ 1 

# d - Pi' '

wher e th e S^'

+ 

### ! ar e Stirlin gnumber s o f th e firs t kind(Abramowit z an d Stegun , 1964) .
Henc e

o • i r+ 1 

### 3 = 1 - P

i 1 2



### - 1 - Pi fo r r = 1

Tabl e 2- 3 give s th e dat a completenes s proportion , p Q 

### , require dt o reduc e th e ris ko f failin gt o detec t two exceedance s t o a specifie dlevel , 3 . Fo r th e 7 5 percen t completenes s criterion , ther e i s a napproximat e 4 4 percen t chanc e tha t a t leas t on e o f th e two exceedance s wil l no t be detected . Fo r th e 5 0 percen t completenes s criterio nth e ris ki s 7 5 percent . I t woul dseem the ntha t th e determinatio no f short-term attain men t statu s require s eithe r nearl y (e.g. , 9 5 percent ) complet e data , o r els e some metho do f inferenc e othe r tha nmerel y countin gth e numbe r o f

8314 6 3 1 3P.4 1



### TABL E 2-3 . Th e proportion , p Q 

### , o f dat a completenes s require d t o limi t th e ris kt o 3 o f no t detectin gr + 1 exceedances .

_ 

### 3 0.0 1 0.0 2 0.0 5 0.1 0 0.2 0 0.4 4 0.7 5 r= 1

0.99 5 0.99 0 0.97 5 0.94 9 0.89 4 0.74 8 0.50 0 r= 3

0.99 7 0.99 5 0.98 7 0.97 4 0.94 6 0.86 5 0.70 7

8314 6 8 

## 1 4P.4 2

exceedance s inth e partia l sampl e (se e EPA , 1979 ; Pac e an dFrank , 1982 ; Thral l an

dBurton , 1983) . Tabl e 2- 3 als o show s th e require ddat a completenes s fo r a three-exceedanc e standard , a topi c t o whic hw e no w turn .

Th e Estimate dNumbe r o f Short-Term Exceedance s

A nalternativ e procedur e fo r determinin gwhethe r short-term ai r qualit y standard s have bee nattaine dat a sit e i s t o extrapolat e fro m th e observe d exceedances , k n- , t o th e tota l numbe r o f exceedances , X- , i.e. , t o estimat e X ^ b

y


# ^ - y P i 

# • Fro m th e conditiona l moment s derive d fo rk^ , w e hav e
E (X i | X

v p. ) = X i

. l ' P i Va r(x

. | X i S p. ) --j

— X i

s o tha t x . i s a

nunbiase destimat e whos e varianc e approache s zer o a s p ^ approache s unity .
Fo r a nr-exceedanc e short-term standard , w e migh t judg e a sit e t o b e in attainmen t o f the standar donl y i f
1 X. < r+ Y•
Thi s procedur e reduce s th e ris ko f failin gt o detec t nonattainmen t (X - > r + 1) , sinc e th e estimat e X ^ i s a nextrapolatio no f th e observe d numbe r o f exceedance s (k^ ) amon gn ^ observe dconcentration s t o th e tota l numbe r o f exceedance s (x. ) inN concentrations . Bu t we have als o intro duce dth e ris ktha t x ^ migh t be overestimated .
8314 6 3 1 5P.4 3



### Thi s procedur e fo r determinin g whethe r a short-ter mai r qualit ystandar d ha s bee nattaine da t a sit e ca n b e cas t a s a statistica l tes t o f a hypothesis
. Asit e i s presume dt o b e i n attainmen t o f th e standar d(i.e. , 1 th e nul l hypothesi s i s tha t x , < r ) unles s x . > r + j » i n whic hcas e i t i s judge dt o hav e faile dt o attai nth e standard , i.e. , we infe r tha t th e alternativ e hypotheis , x . > r + 1 , holds .
A"Typ e I " erro r occur s whe nth e sit e ha s actuall yattaine dth e standar d bu t w e incorrectl yinfe r tha t i t ha s not . Previously , whe nw e determine d attainmen t solel yo n th e basi s o f th e observe dnumbe r o f exceedances , k^ , thi s wa s impossible , sinc e k ^ ca n neve r b e greate r tha nth e actua l numbe r o f exceedances , x i 

### . Now , however , th e probabilit yo f a Typ e I erro r i s a , wher e


## P r 

# (v r +

# iiv r 

# )

- Whateve r th e proportio no f dat a completeness , p^ , th e probabilit yo f a Typ e I erro r wil l b e greates t whe nth e sit e i s jus t barel yi n attainmen t o f th e standard , i.e. , whe nx . = r , i n whic hcas e

a = P r (x- > r + Y

### | X . = r\
. 1 No wi f p ^ > 2r/(2 r + 1 ) i t i s impossibl e fo r x ^ t o equa l o r excee dr + j sinc e

1 

### P i

<r 

### (2r)/(2 r + 1 )



# - 4

s o tha t

a = 0 i f p . > 2r/(2 r + 1 )
8314 6 3 1 6P.4 4



### I f 2( r - l)/(2 r +1 ) < p i 

### < 2r/(2 r + 1) , x i 

### wil l equa l o r excee dr + ^ onl y i f k . = r . I

ngeneral , a s p ^ become s smaller , fewe r observe d

' A 

### exceedance s (k^ ) ar e require dt o produc e anestimat e (x- ) equalin g or exceedin gr + j

, an dth e probabilit y

(a ) o f a Typ e I erro r increases , a s show ninTabl e 2-4 . O f course , whe nth e sampl e siz e (n.,- ) fall s below r , th e observe dnumbe r o f exceedance s (k^ ) may be no mor e tha nn^ an dth e ris ko f a Typ e I erro r i s reduced . (W e assume throughou t thi s discussio n tha t N > ( r + 1) ( r + j)

s o tha t ( r + 1)/ N

< 2/(2 r + 1). ) Th e binomia l approximatio ngive ninTabl e 2- 4 i s accurat e s o longa s N i s larg e relativ e t o r .

A 

### Thus , basin ga

nattainmen t determinatio no nx , rathe r thank^ introduce s some probabilit y

(a ) o f a Typ e I error , bu t th e maximu m probabilit y

(3 ) o f a Typ e I I erro r i s substantiall y reduce dfo r smal l value s o f p^ . We hav e (^ i < r+ | | X i = r+ l ) 

# fc 1 

# Ir+ 

# •SNT-i-iV W

f o r2 

### ( J ' * ? < p 

### < ?j

_ T o r

### 2 r+ 1 p 

### i * 2 r+ 1

an d fo rj = 1 , 2 , 3 , ... , r+ 1 

# IIMP^-P, ' r+l- k

O f specia l interes t ar e th e case s r = 1 , th e one-exceedanc e for m o f short-term

standards , an

dr = 3 , a three-year , three-exceedanc e for m o f

8314 6 3 1 7TABLE 2-4

. Th e maximu m probability , a , o fa Typ e I erro ran d th e binomia l approximatio n t o th e probability , a s

function s o fth e proportio n P i o fdat a completeness

. P i Interva l Pr(x i > r+ j \ x. . = r ) Binomia l Approximatio n 

# irhr < P i « ! ° °

r - •• r - ' .r- k 

# |IL^ 1 < p, < ^

, J

. 2 

# . 3

. £( k r 

# )(nV-V("i ) £^ " < U 

# " >'> '

N K i N N

8314 6 r

### - - - r

### - ' 

### r-k

i—» 0 0 

### n . n . i < D = 

### !l l 

### < LV/ r 

### \ / N 

### - r\ // N 

### \ V 1 

### /r \ 

### „ k M 

### „ ^r- k 

# S (k%VA)/

ws o p?«-»,) '

O lth e standards . Table s 2- 5 and 2- 6 sho wth e binomia l approximation s t o a

an d 3 fo r r = 1 and r = 3 , respectively
. Th e correspondin g plot s ar e shown i n Figure s 2- 1 and 2-2 .

Curren t Practic e i n Determinin g th e Short-Ter mAttainmen t Statu s o f a Sit e

In th e previou s sectio n w e calculate d a and 3 , th e probabilitie s o f misclassifyin g attainmen t o r nonattainmen t sites , respectively , i f th e A estimate d numbe r o f exceedance s (x. = ) i s rounde d t o th e neares t intege r and the n compare d t o th e allowe d numbe r o f exceedance s (r) . Thi s procedur e generate d th e regularl yspace d dat a completenes s cutpoint s 2j/(2 r + 1 ) o r j/( r + 0.5 ) o f Tabl e 2-4 . In fact , th e curren t EP Apolic yi s t o round \. t o th e neares t tent han d the n compar e th e rounde d valu e t o r . Tha t is , th e statu s o f a sit e i s classifie d a s nonattainmen t onl yi f X i , rounde d t o th e neares t tenth , exceed s r . Thi s procedur e generate s th e dat a completenes s cutpoint s j/( r + .05 ) whic har e greate r tha n th e correspondin g cutpoint s j/( r + 0.5 ) o f Tabl e 2-4 .
Fo r a sit e wit hdat a completenes s proportio n betwee n j/( r + 0.5 ) and j/( r + .05) , roundin g t o th e neares t tent hrathe r tha n t o th e neares t intege r thu s increase s th e chanc e o f misclassifyin g th e statu s o f th e sit e a s nonattainmen t i f i n fac t th e tota l numbe r o f exceedance s i s r . On th e othe r hand , roundin g t o th e neares t tent hdecrease s th e chanc e o f mis

classifyin g th e statu s a s attainmen t i f th e tota l numbe r o f exceedance s i s actuall yr + 1 .

Limitin g th e MaximumProbabilit yo f a Typ e I Erro r

A Th e effec t o f determinin g attainmen t statu s b ycomparin g X
- t o th e fixe d 1 1 valu e r + yi s t o creat e th e regularl yspace d dat a completenes s cutpoint s 2j/(2 r + 1 ) o f Tabl e 2-4 . I f Pi = 2r/(2 r + 1) , th e maximumprobabilit ya

8314 6 3 

### 1 9

### TABL E 2-5 . Binomia l approximation s t o probabilitie s o

t and3 i nth e one-exceedanc e case , r = 1 .
P i interva l 2/ 3 < P i < 1 0 < p i < 2/ 3 Binomia l a 0 P i 

### Ap

p roximatio n 3 i-p, 2 d - Pj) 2

8314 6 8 2 0•

•

•

•

•

•

•

•

•

•

• 8314 6 i n - 1 . u

0. 6

0. 6
Q . T J 0. 4

0. 2 

# °-8 . '

i ' i i i < i 

# : A

. / / ! 0 0. 2 0. 4 0. 6 0. 3 Pi 

### (a ) a versu s p ^
1. 0

0 . s

0 . 6 *->
X J 0 . 4

0 . 2


# °- 8 \

; \

_\

: \

\ 

# •\ J

i i i i > i i i 0 0. 2 0. 4 O. S 0 . b P i 

### (b ) 3 versu s p i



### FIGUR E 2-1 . Binomia l approximatio nt o a th e one-exceedanc e case , r = 1 . 2 1 -

-

-

- ]
..- ,

1 

## o

ande inTABL E 2-6 . Binomia l approximation s t o probabilitie s a an d3 inth e three-exceedanc e case , r = 3 .

P i Interva l Binomia l Approximatio n

6/ 7 < p . 1 1 - Pi

P O r o 4/ 7 < p i < 6/ 7 6 Pi 2 ( 1 - Pi) 2 + 4 p . ( 1 - Pi ) 3 + ( 1 - Pi ) 4

2/ 7 < p . < 4/ 7 p. 3 + 3 p. 2 ( 1 - p. ) 4 P i ( 1 - Pi) 3 + ( 1 - Pi) 4

3/ N < p . < 2/ 7 1 - ( 1 - Pi) ' ( 1 - Pi )

P i = 2/ N 3 Pi 2 ( 1 - .p. ) + 3 p . ( 1 - Pi ) 2 ( 1 - Pi )

P i = 1/ N 3 P j ( 1 - Pj) ' ( 1 - Pj )

8314 61. 0

0. 8

0. 6
Q . 0. 4

0.2

- " i 1 1 i 1 1 1 r

(a ) a versu s pi

1. 0

o.e 0.6 L

c u

. D 0 . 4

0.2 r

0. 0 0. 0 0 .
(b ) 3 versu s P i

FIGURE 2-2 . Binomia l approximation s t o a an d6 in th e three-exceedanc e case , r = 3 . 8314 6 

### 2 3P.5 1



### o f a Typ e I erro r wil l be betwee n0.6 0 an d0.67 , dependin gonr . Th e poin t i s tha t b

y usingthi s metho do f determinin gthe short-term attain men t statu s o f a sit e we ru na highris ktha t th e statu s migh t b e errone ousl y determine da s nonattainment .
It i s possibl e t o modif y th e metho ds o tha t th e probabilit y o f a Typ e I erro r neve r exceed s a prescribe dlevel , ag , a t th e cos t o f increasin gth e probabilit y

o f a Typ e I I erro r (inwhic ha site' s failur e t o attai nth e short-term

standar dgoe s undetected) . The modificatio ni s t o simpl y shif t th e dat a completenes s cutpoint s t o smalle r values , n, , n 2 

### , ... , n, whic h ar e obtaine dfrom

ag . The procedur e i s depicte dinTabl e 2-7 . Th e resultin gmaximu

m probabilit y

o f a Typ e I erro r i s tha t give ninTabl e 2-4 , wit h2j/(2 r + 1 ) replace db y IT, , fo r j = 1 , 2 , ... , r . The maximu m J 

### probability , 3 , o f a Typ e I I erro r i s als o obtaine di nthi s wa yfro m previou s expressions
. Belo ww e demonstrat e th e derivatio no f n j fro ma prescribe dleve l o f risk , ag , fo r th e case s r = 1 andr = 3 . Th e procedur e fo r th e curren t one-exceedanc e for mo f a standar d( r = 1 ) i s depicte di nTabl e 2-8 . W e se e tha t a Typ e I erro r ca nonl yoccu r i f k i = 1 , P i < Ttj , andX i = 1 . Th e probabilit yo f a Typ e I erro r i s thu s zer o i f p n- > TTJ . I f P i < it
. the n


### P p 

### (k i 

### = 1 | x i 

### = 1 ) = p. ,

s o th e absolut e maximu m(i.e. , acros s al l sites ) occur s a t P i = n^ . W e maytherefor e limi t th e ris k o f a Typ e I erro r t o a g simpl yb ysettin g n 1



### = ag . Figur e 2- 3 depict s a and3 a s function s o f P i whe nth e prescribe d absolut e maximu mprobabilit yo f a Typ e I erro r i s a g = 0.20 .
I nth e cas e r = 3 , k i wil l b e take na t fac e valu e i f P i > 113 , i.e. , w e wil l onl ydeclar e th e statu s t o b e nonattinamen t i f w e observ e k i 

### > 4

8314 6 3 2 4P.5 2

TABL E 2-7 . Attainmen t statu s a s a functio no f observe dexceedance s (ki ) an d th e proportio n(pi ) o f dat a completeness .

*
0 1 1 2 0 < p . < 1

0< P i< TT

j
* 1 < P i < 1 0< P

n- < TT

2 Assigne d k i P i Interva l Statu s A N A N ir 2 < P i < 1 A

r0 < p i < ir rN rTT P < p i < 1 A > r + 1 0<Pi< lN
A= attainment , N = nonattainmen t

TABLE 2-8

. Attainmen tstatu s fo rth e one-exceedanc e for m o fth e standar d ( r= 1 )

k i 0 1 1 > 2 P i Interva l

0 < P i < 1 0 < P i < TT

j T^ X< P i < 1 0 < P i < 1 Assigne d Statu s A N A N
A= attainment , N = nonattainmen t

25
8314 6 8P.5 3

1. 0

O.B r

0. 6 sz a. 0. 4 -

0. 2

1. 0 

### (a ) a

versu s p i



### (b ) 3 versu s p .

FIGUR E 2-3 . Binomia l approximatio nt o a and 3 fo r r = 1 and a specificatio no f a Q 

### = 0.2 .
8314 6 

## 26P.5 4



### exceedances . Henc e ther e i s no chanc e o f a Typ e I error . I f n g< Pi < 113 , thre e observe dexceedance s ar e require dfo r a nonattainmen t determina tion , andth e maximu

m probabilit y

o f anincorrec t nonattainmen t determina tio ni s approximatel y p* . Continuing , we obtai na modifie dTabl e 2- 6 wit h cutpoint s 2j/ 7 replace db y H Jfo r j = 1 , 2 , 3 . Clos e approximation s t o th e require dn ; ar e obtaine db y solvin g 3 a 

### 0 " * 3

a 

### 0 = 

### ^ 2 + 3ir 

### 2 ^ " v



### 2^

a g = 1 - ( 1 - Hj )


### Fo r a g= 0.20 , w

e obtai n it

3 

### = 0.58 5
ir 2 

### = 0.28 7
ir 1 

### * = 0.07 2 Figur e 2- 4 show s th e resultin ga and3 .

Summary o f Result s an

dAlternativ e Approache s
I ndiscussin gattainmen t o f ai r qualit y standard s a t a

nindividua l sit e durin ga specifie dyea r (or othe r attainmen t period) , we have adopte dth e view tha t a complet e se t o f measure dambien t concentration s a t th e sit e fo r th e yea r woul dunambiguousl y defin e th e statu s o f th e sit e t o b e eithe r tha t o f attainmen t o r nonattainment . We hav e als o adopte dth e view that , howeve r incomplet e th e dat a may be , we mus t come t o a decisio n regardin gwhether , o

nth e basi s o f a partia l se t o f observations , th e statu s appear s t o be eithe r tha t o f attainmen t o r nonattainment , i.e. , w e hav e exclude da

n"indeterminate " status . Rathe r tha nprescribin ga dat a
8314 6 3 2 7P.5 5

1. 0

0. 6

0.6

-
CL 0. 4

0. 2

0. 0

1. 0 

### (a ) a versu s p .

1 . ~ !



### (b ) 3 versu s p .

FIGUR E 2-4 . Binomia l approximatio nt o a an d 3 fo r r = 3 an

d a specificatio no f a n

### = 0.2 . 8314 6 

## 2 8P.5 6



### completenes s threshhold , pg , abov e whic hwe may reliabl y

asssig nattain men t status , we have addresse dth e chanc e o f comin gt o anerroneou s decisio na s a functio no f th e dat a completenes s proportion , Pi , whateve r i t may be . Moreover , we hav e focuse do nsituation s inwhic hth e tota l numbe r o f exceedance s (x. ) i s jus t withi no r jus t abov e tha t prescribe db y th e standard , i.e. , situation s inwhic hth e chanc e o f misjudgin gth e attainmen t statu s i s greatest . Withi nthi s context , we examine dthre e procedure s fo r judgin gattainmen t status : (1 ) base dsolel y o nth e observe dnumbe r o f exceedances , k- ; (2 ) base dsolel y

o nth e estimate dnumbe r o f exceedances , x . = k./p. ; an d(3 ) base do nbot hk

i an dth e dat a completenes s proportion , Pi . I nth e firs t procedur e ther e i s no possibilit y o f a Typ e I error , i nwhic hth e statu s i s erroneousl y

determine dt o be tha t o f nonattainment . I nth e second procedur e th e maximu

m probability , a , o f a Typ e I erro r i s greate r than 0.6 , but th e probability , 3 , o f a Typ e I I error , inwhic hthe statu s i s erroneousl y

determine dt o b e tha t o f attainment , i s reduce dfo r moderat e dat a completenes s porportions . Inth e thir dprocedur e a i s limite dt o a preassigne dleve l a ga t th e cos t o f increasin g3 . Th e Typ e I/Typ e I I erro r analysi s doe s no t giv e a definitiv e answe r regardin gwhic ho f th e thre e procedure s i s preferable , becaus e thi s depend s o

nth e prioritie s o f th e decisionmaker . Fo r example , i f a Typ e I erro r i s t o be completel y avoided , th e firs t procedur e i s preferable . It i s hope dtha t th e analysi s wil l nevertheles s be usefu l inconsiderin gth e interpla y

betwee nth e proportio no f dat a completenes s an dth e likelihoo d o f a nerroneou s attainmen t determination .
Frankan dCurra n(1982 ) presen t a simila r analysi s inth e cas e o f particu lat e matter , whic hi s typicall y sample donc e every

si xdays . Thei r result s correspon dt o thos e we hav e obtained(althoughthey explicitl y trea t a five-exceedances-per-year , three-yea r standard , whic hwe trea t onl y implicitl y

inTabl e 2-4 , settin gr = 15) . Moreover , th e theoretica l probabilitie s they

calculat e correspon dt o thos e the y obtaine dempiricall y

8314 6 3 2 9P.5 7



### from a subse t o f site s havin gnearl y complet e data . Thei r stud y empha size s a compariso no f th e firs t two procedures , base do nk i and X. , respectively . They show tha t whe nth e numbe r o f exceedance s i s wel l abov e th e numbe r (r ) allowe di nth e standard , ou r enhance dabilit y t o detec t nonattainmen t usingth e secondprocedur e i s eve ngreate r tha ntha t show nb y th e presen t worst-cas e analysi s (inwhic hx , = r + 1) .
A differen t for

m o f theoretica l analysi s i s presente db y Davidso nan

d Hopk e (1983) . Th e author s construc t a probabilit y distributio no f pollutan t concentration s at a give nsit e havin ga characteristi c concen tration , c , tha t i s exceede do naverag e onc e pe r year . The y defin e th e statu s o f a sit e a s "attainment " i f c i s at or below th e standard ; otherwis e i t i s "nonattainment. "

It shoul dbe note dtha t theoretica l analysi s o f th e mos t sensitiv e scenario s i s no t th e onl y possibl e way

t o view

th e interpla y

betwee ndat a completenes s an

dprocedure s fo r inferrin gattainmen t status . A nalterna tiv e i s t o empiricall y estimat e th e probabilit y o f misclassifyin ga sit e wit ha give nproportio no f dat a completeness . Thi s approac hi s discusse d inSectio n3 . Ther e ar e othe r procedure s fo r determinin gattainment . Breiman , Gins , an d Ston e (1978 ) substantiall y ameliorat e th e effect s o f incomplet e dat a usin g a differen t estimate , X- , o f th e actua l numbe r o f exceedances , Xi « Thei r approac hi s t o fi t anexponentia l distributio nt o th e highes t 2 0 percen t observe dconcentration s an dt o us e thi s fitte dtai l exponentia l distribu tio nt o estimat e th e expectednumbe r o f exceedance s amon gth e missin g concentration s (als o se e Breima nan dStone , 1983) . It migh t prov e worthwhil e t o includ e a procedur e base do nx . i nfutur e investigation s o f th e effect s o f incomplet e dat a o nth e chanc e o f misclassification .

8314 6 3 3 0P.5 8



### EMPIRICA L ESTIMATIO NO F TH E PROBABILIT YO F MISCLASSIFYIN GATTAINMEN T STATU S

LONG-TER MAMBIEN T STANDARD S
Usin g th e statistica l framewor k introduce di n Sectio n2 , w e conside r th e it hsit e t o hav e attaine da long-ter mambien t standar di f th e averag e o f a complet e se t o f concentrations , \x^, doe s no t excee dth e referenc e concentration , C NAAQS * Typically , a complet e se t o f concentration s i s no t recorded , i.e. , th e dat a completenes s proportio n(Pi ) i s les s tha nunity , s o tha t th e averag e o f th e recorde dconcentration s (u• ) i s use dinstea do f u ^ t o classif yth e sit e a s attainmen t o r nonattainment
.
I f P i i s les s tha nunit yand th e sit e ha s i n fac t attaine dth e standard , ther e i s a chance , denote db yai , tha t th e sit e wil l b e misclassifie da s nonattainment
. Similarly , i f th e sit e ha s i n fac t faile dt o attai nth e standard , ther e i s som e chance , 3. , tha t th e sit e wil l b e misclassifie da s

' A 

### attainment
. Usin g th e approximat e normalit yo f y i 

### discusse di n Sectio n2 , i f

## " 1 < 

### SAAQ S we hav e

a 

### i = p r

### <" 1 > SAAQS )

* 1

= $ Np , 

### ,1/ 2

1 " P i Np , 

### vl/ 2 C NAAQ S " M i

U 

### 1 " SAAQ S 

## ° 1

8314 6 1 0 

### 3 1P.5 9



## andi f U i > C NAA Q S 

## w e hav e

* 1 = P r

## K < C 

## NAAQS } 

# -[(A T ,1/ 2

,1/ 2 1 

## NAAQ S '" j a

w 

## i " C 

## NAAQ S
Settin g

N p 

## i \ 1/ 2 

## " 1 " C 

## NAAQ S 1 

## " P i i A 

## wher e a . i s th e standar ddeviatio no f th e recorde dconcentrations , w e ma y estimat e a i 

## and3 . a s

a 

## i = 

## * ( 

## H } i f p 

## i < C 

## NAAQ S

e i =»(- C l ) i fu 1 >c NAAQ S



## We the nestimat e a(pg ) and3(p Q 

## ) » th e probabilit y o f misclassifyin ga sit e whos e actua l statu s i s attainmen t an dnonattainment , respectively , a s
a(p Q 

## ) = averag e a^

, p i 

## >

p Q

## an dU i 

## < C NAAQ

S
3(p 0 ) = averag e jj. , P i > p Q an d u . >

C NAAQ S

8314 6 1 0 

### 3 2P.6 0



### The calculatio no f a(p Q 

### ) an d3(p Q 

### ) require s tha t eac hsit e b e tentativel y classifie da s attainmen t o r nonattainmen t base donu i 

### prio r t o th e AA' 

### calculatio no f a i 

### o r 3. . Thi s tentativ e classificatio nmay be erroneou s

* ' A 

### fo r a give nsite , particularl y i f p . i s nea r Cm A A 

### Q$ , bu t averagin gove r a larg e numbe r o f site s produce s estimate s a(p Q 

### ) , 8(p 0 

### ) whos e reliabilit y i s muc hgreate r tha nth e correspondin gestimates , a i 

### o r 3. , a t a give nsite . Tabl e 3- 1 summarize s th e result s o f thes e calculation s fo r sulfu r dioxide , nitroge ndioxide , lead , andtota l suspende dparticulat e matter . I nsome case s no estimat e i s give nbecaus e th e numbe r o f site s i s insufficien t fo r reliabl e estimation . I nth e case s wher e reliabl e estimatio ni s possible , AA 

### w e se e tha t a(pg ) and3(Pg ) ar e les s tha non e percent . Fo r sulfu r dioxid e andnitroge ndioxide , dat a completenes s requirement s o f 50 , 75 , and9 5 percen t wer e examined . Fo r leadan dtota l suspende dparticulate , whic h ar e typicall y

monitore donc e pe r si xdays , we examine ddat a completenes s requirement s o f 5 percen t (correspondin gt o th e curren t requiremen t o f fiv e recorde dconcentration s pe r quarter) , 1 2 percen t (whic hmay be interprete da s requirin ga 7 5 percen t dat a recover y fro m a onc e pe r si x day samplin gschedule) , and2 5 percen t (7 5 percen t recovery from a onc e pe r 3 day

samplin gschedule) . Whe ninterpretin gTabl e 3- 1 i t i s importan t t o distinguis hbetwee nrate s o f misclassificatio nthat ar e possibl e unde r variou s dat a completenes s requirement s an

drate s tha t ar e estimate dfrom 198 1 data . The distinctio n i s especiall y

importan t fo r leadandparticulat e matte r sinc e thei r once per-six-da y

samplin gschedule s limi t th e dat a completenes s proportio nt o one-sixth . Figure s 3- 1 throug h3- 3 illustrat e thi s poin t for , respectively , th e quarterl y

leadstandard , th e annua l TS P standar d(basedonth e annua l geometri c mea nconcentration) , an da hypothetica l TS P standar dbase don th e annua l arithmeti c mea nconcentration . The las t figur e i s include d becaus e anannua l particulat e (P M 10 ) standar dbase donanarithmeti c mea n i s currentl y

unde r consideration .
Eachfigur e consist s o f a se t o f curves , a se t o f plotte dpoints , an da ba r chart . Eachcurv e show s possibl e misclassificatio nprobabilitie s fo r
83146 r 1 0 3 3P.6 1



### TABL E 3-1 . Estimate dprobabilit y tha t a site' s attainmen t statu s fo r long term standard s i s misclassifie da s a functio no f minimu m dat a completenes s proportion , pg . Th

e tabl e entrie s ar e th e estimate dpercentages o f attain men t site s (A ) meetin ga givendat a completenes s requirement , p 0 

### , likel y t o be misjudge da s nonattainment , o r th e percentag e o f nonattainmen t site s (N ) likel y t o b e misjudge da s attainment . N o estimat e i s mad e i f th e numbe r o f site s i s insufficient . (Note : ther e ar e no know nspecifi c instance s o f misclassifie dsites—se e tex t fo r a discussio no f th e estimatio nprocedure. )

S0 2 

### (annual) : 89 1 site s Assigne d Statu s

Actua l p 0 = .5 0 (85%) ap Q = .7 5 (71% ) p 0 = .95 ° (8% ) Statu s A N A N A N A (99%) c< l% d< 1 % < 1 % N(1% ) N0 2 (annual) : 32 4 site s

Actua l Statu s A(97% ) N(3% ) p 0 = .5 0 (78% )

AN
< 1 % p 0 = .7 5 (54% )

AN
< 1 % p

g = .9 5 (8% ) AN
< 1 %

Pb (maximu m quarterly) : 69 6 site s

Actua l p 0 = .0 5 (61% ) p 0 = .1 2 (39% ) p Q = .2 5 (1% ) Statu s A N A N A N A(97% ) < 1 % < 1 % N (3% ) 

### TS P (annual) : 365 4 site s

Actua l Statu s A(87% ) N(13% ) p 0 = .0 5 (92%) e

AN 2 % 9% f Pg = .1 2 (77% ) AN 2 % 8 % Pg = .2 5 (6% ) AN
< 1 % 3 % 

### Note s o n nex t page .

83146 T 8 3 4P.6 2



## a

d Note s fo r Tabl e 3- 1

Th e numbe r inparenthese s i s th e percentag e o f site s meetin gth e require d dat a completenes s proportion , pg . B y 0.9 5 th e valu e y^g - = 0.952 4 i s meant . The numbe r inparenthese s i s th e estimate dpercentag e o f site s tha t hav e attaine do r faile dt o attai nth e standard . Estimate s ar e base do nreporte d long-term

averages . Thi s i s th e estimate da-probabilit y tha t a sit e inattainmen t an dmeetin g th e dat a completenes s proportio npg= 0. 5 woul dbe misjudge d a s nonattainment . Thi s entr y ca nals o b e interprete da s anestimat e o f th e percentag e o f suc hsite s expecte dt o be misclassified .

e 

### Not e tha t intermitten t monitorin ginitsel f result s inles s tha ncomplet e data . Th

e dat a completenes s requirement s o f 0.05 , 0.12 , an d0.2 5 correspon d approximatel y

t o fiv e recorde d24-hou r measurement s pe r quarter , and7 5 percen t dat a recovery fro m once-per-six-da y

an donce-per-three-da y

samplin g schedules , respectively .

' Thi s i s th e estimate d3-probabilit y tha t a sit e no t inattainmen t o f th e standar dan

dmeetin gdat a completenes s proportio np g= 0.0 5 woul dbe misjudge da s attainment . Alternatively , we ca ninterpre t thi s entr y a s an estimat e o f th e percentag e o f suc hsite s expecte dt o be misclassified .

83146 T 8 3 5P.6 3

(A ) f Q = 1/18 , N = 91 ; (1/18 ) < = P i < (1/12) , 5 0 site s E. 5 5: . o/v o/v o/v 0.3 6 (10 % tile ) 0.5 9 (50 % tile ) j 0.9 3 (90 % tile )

E. 5 (B ) f

Q= 1/12 , N= 91 ; (1/12 ) < = p . < (1/8) , 8 6 sites

. 1 E

. 'C£ - - .IS

E.5
-a ssMiscl

*• - o > > * - > • - < D o E 2 Z E E 

## --r -7

O • 

# T

. i

_ ? .-^ i I o/v = 0.3 6 (10 % tile ) J o/v = 0.5 9 (50 % tile ) \ o/v = 0.9 3 (90 % tile ) ~ ~

I 

# i

E. 3
. 2 2

. 4
(C ) p Q = 1/8 , N = E.~
- ! 0 . 4 ; 9 1 ; (1/8 ) < = p . < = (1/6) , 18 0 sites ,

o

J 3 O 0 . 1 0 . 2 -0. 1 0 . 0 1. 2 o/ p = 0.3 6 (10 % tile ) - j o/v = 0.5 9 (50 % tile ) | o/v = 0.9 3 (90 % tile ) I

h 3 . 3 Figur e 3-1 . Lead : th e probabilit y o f misjudgin gactua l attainmen t statu s a s determine db

y th e mea n(y) , th e coefficien t o f variatio n(o/p) , andth e proportio no f dat a completenes s (p) . Continuou s curve s show probabilitie s determine db

y fixe dcombination s o f pan do/p , wherea s th e plotte dpoint s ar e determine db

y combination s obtaine dfro m 198 1 data . 

### 3 5P.6 4



### (A) p n

### = 1/18 , N c o

• J I O o H

vt vt m r — U yt X. H

O > > * J
r — J 3 « 0 O 0 0
. 0
. 0
. 0
. B
. 0
. 6 5 4 3 2 1 0 

### 365 ; (1/18 ) < = p . < (1/12) , 19 9 sites
. i » . Percen t 00 s se .

" 0 - 0
. j

_

30. 0 _ L l n og/l n y g « 0.0 8 (10 % tile ) l n og/l n y g = 0.1 0 (50 % tile ) l n o g / l n y g = 0.1 5 (90 % tile )

_ L 60. 0 90. 0 _ L 120. 0 w,#//Mv.'TWA -'AM



## is?# r>T M
150. 0


### (B ) p Q

### = 1/12 , N= 365 ; (1/12 ) < = p i

### < (1/8) , 43 2 sites , § 0. 6 S 0. 5 T ; 0. 4 tA n a0- 3 < ^ 0. 2 o Percen t Ob s 5B

. I

.£ > O 0. 1 0. 0 -0. , _ L

30. 0 rititih

J

_ I n o g / l n y g = 0.0 8 (10 % tile ) - I n og/l n v

g = 0.1 0 (50 % tile )

_ l n o g / l n u g = 0.1 5 (90 % tile )

mirrW-y.

_ L

_ L 60. 0 90. 0 120. 0 150. 0


### (C ) p n

### = 1/8 , N= 365 ; (1/8 ) < = Pi K 

### (1/6) , 212 5 sites , 0. 6 Percen tOD s se . i I n og/l n y g •

= 0.0 8 (10 % tile ) - l n og/l n v

g = 0.1 0 (50 % tile )

—l n o g / l n y g = 0.1 5 (90 % tile )

150. 0 Figur e 3-2 . TSP , annua l geometri c mean : th e probabilit y o f misjudgin g actua l attainmen t statu s a s determine db y th e natura l log o f th e geometri c mea n(p q ) . th e coefficien t o f variatio n(l no g /l np g ) , an dth e proportio n o f dat a completenes s (p) . Continuou s curve s show probabilitie s determine d b y fixe dcombination s o f pan d(I nOg/l npn) , wherea s th e plotte dpoint s ar e determine db

y combination s obtaine dTro m 198 1 data . 

### 37P.6 5

(A ) p Q = 1/18 , N 

### = 365 ; (1/18 ) < 0.6 j 

### p . < (1/12) , 19 9 site s Percen t Obs SB.
o g 0.5 - £0. 4 -
•s o 0. 3 -

*

_ B. 2 o 

### 2 B. l

• S 0- 0 O £-0. 1 30. 0 i o/v' 0.3 2 (10 % tile ) o/v ' 0.4 2 (50 % tile ) o/v •0.6 1 (90 % tile )

£** ••>*'") » #*••* •

_ L J

_ 60. 0 90. 0 120. 0 M S I
150. 0
(B ) p Q = 1/12 , N = 365 ; (1/12 ) < = p . < (1/8) , 43 2 sites , 0 . 6 1 o/v = 0.3 2 (10 % tile)
- o/v = 0.4 2 (50 % tile ) o/ y= 0.6 1 (90 % tile ) Percen tOb s 5B . IBB .

- H

° - -0. : 0 . 0 30. 0 60. 0 90. 0 _ L 120. 0 150. 0

g-0. 6 S 0. 5
< 4 - 

### s 0


### - 4 a 0. 3 (C ) P 0= 1/8 , N = 365 ; i (1/8 ) < = p . < = (1/6) , 212 5 sites

. e i i i

m o/v = 0.3 2 (10 % tile ) - S o/v

- 0.42 (50

% tile )
* * S o/v - 0.6 1 (90

% tile) " 

# * fir * Percen t Obs 5B . I IB B 1 1

<t 0.2!
- O i
•• ? B. lU

• o 0. 0 o I . ° - - B J. 0 MTX. VP T I '-<(".. C I 60. 0 90. 0 120. 0 150. 0 

### Figur e 3-3 . TSP , annua l arithmeti c mean : th e probabilit y o f misjudgin g actua l attainmen t statu s a s determine db y th e mea n(p) , th e coefficien t o f variatio n(a/p) , andth e proportio no f dat a completenes s (p) . Continuou s curve s show

probabilitie s determine db y fixe dcombination s o f pan da/p , wherea s th e plotte dpoint s ar e determine db y combination s obtaine dfro m 198 1 data . 

## 3 8P.6 6

a rang e o f mea nconcentration s b y combinin ga give ndat a completenes s proportio n(1/18 , 1/12 , o r 1/8 ) wit ha give ncoefficien t o f variatio n (obtaine dfro

m th e 10th , 50th , o r 90t hpercentil e o f th e 198 1 distribu tion) . The give ndat a completenes s proportion s approximatel y correspon d t o 5 , 8 , an

d1 2 sample s pe r quarter , respectively . The las t proportion , 1/8 , may

als o be interprete da s a 7 5 percen t dat a recover y rat e fro

m a once-per-six-da y

samplin gschedule .
The plotte dpoint s ineac hfigur e represen t site s whos e dat a completenes s proportion s fal l wit ha specifie dinterval . Part s (A) , (B) , an d(C ) o f eac hfigur e represen t completenes s interval s o f 1/1 8 t o 1/12 , 1/1 2 t o 1/8 , an d1/ 8 t o 1/6 , respectively . Fo r eac hsit e th e estimate dmisclassifica tio nprobabilit y

(inpar t determine db y th e coefficien t o f variatio n reporte dfo r tha t site ) i s plotte dagains t th e reporte dmea nconcentra tion . Withi neac hdat a completenes s interva l th e ba r char t show s th e percentage s o f site s havin ga give nprobabilit y o f misclassification .
The figure s sho

w tha t th e dat a completenes s requirement s b y themselve s do no t limi t probabilitie s o f misclassification . It canbe seen , however , that fo r th e grea t majorit y o f 198 1 sites , reporte dmea nvalue s ar e sufficientl y

fa r abov e o r below th e standar dan

dcoefficient s o f variatio n ar e sufficientl y

smal l s o tha t th e estimate dprobabilitie s ar e effectivel y zero . Thus , misclassification s ar e no t rule dou t b y thes e dat a complete nes s requirement s bu t the y ar e unlikely , assumin gtha t th e mean s an d coefficient s o f variatio nreporte din198 1 are representativ e o f othe r years .
We ca nals o us e th e figure s t o infe r th e benefit s o f requirin g1 2 rathe r tha n5 sample s pe r quarte r fro m intermitten t samplers . I nth e cas e o f lea dw e ar e assessin gth e benefit s o f curren t requirement s sinc e 1 2 sample s pe r quarte r ar e require dt o determin e attainmen t statu s (se e Tabl e G-1) . The ba r chart s o f part s (A ) an d(C ) o f Figur e 3- 1 show tha t th e two group s o f site s hav e nearl y th e same lo

w susceptibilit y

t o misclassifica tion . The curve s distinguis hpart s (A ) an d(C ) mor e clearly , however , an d ar e probabl y

mor e indicativ e o f th e futur e consequence s o f strengthenin g
83l46 r 1 0 3 9P.6 7

o rrelaxin g dat a requirement s fo ral l sites

. W e ca n expres s thi s differ
enc e b ycomparin g "critica l concentratio n intervals, " i.e. , b ynotin g th e interva l o fmaximu m quarterl ymean concentration s withi n whic h th e probabilit yo fmisclassificatio n exceed s 0.1 0 fo ra sit e havin g a typica l (i.e. , median ) coefficien to fvariation

. Thi s definitio n o fa "critica l interval " bear s some explanation

. W e ar e intereste d i n correctl yclassifyin g th e attainmen tstatu s o fa sit e whos e actua l long-ter m averag e concentration , p , i s estimate d byp base d o n incomplet e data

. Fo rsimplicity , w e assum e th e coefficien to fvariation , a/p , t o b e th e media n o fth e coefficient s o fvariatio n calculate d fo rou r sampl e o fsites

. I fp i s ver yclos e t o th e referenc e concentration , ^NAAQS ' t ^ 1e n ww ^ l be abov e o rbelo wC^/m s wit h nearl yequa l proba
bility

. Therefore , i fp i s onl yslightl yabov e o rbelo wC N ^g$ , th e ris k o fmisclassificatio n wil l b e clos e t o 5 0 percent
. A s p moves awa

yfro m ^NAAQS » t,1 e probabilit yo fmisclassificatio n decrease s until , a tsome poin tabov e an d belo wC^g^ , th e probabilit ydrop s belo w1 0 percent
. Th e critica l interva l i s thu s th e se to fp fo rwhic h attainmen tdetermination s ar e subjec tt o erro rwit h nonnegligibl e probability

. I n mathematica l notatio n w e defin e th e critica l interva l as th e unio n o f interval s Aan d B wher e

A= { u : P < C NAAQ S an d Pr p ( p > C NAAQS ) > a }

B = { p : P > C NAAQ S an d Pr p ( p < C NAAQS ) > a j

s o tha tth e critica l interva l i s

A U B = { p : a < Pr y ( J > C NAAQ$ ) < 1 - a }

. I n th e framewor k

o ftestin g th e statistica l hypothesi s tha tp <. Cw/\ng c base d o n p , th e critica l interva l i s th e se to fp fo rwhic h th e powe ro f

83146 T 1 0 4 QP.6 8

th e test , i.e. , th e probabilit ytha tp exceed s C N ^ A Q$ , i s betwee n a an d 1 - a , i.e. , settin g a equa l t o 0.1 , betwee n 1 0 an d 9 0 percent
. W e ma yals o describ e th e critica l interva l as th e interva l withi n whic h th e probabilitie s o fapparen tattainmen tan d nonattainmen t(base d o n a compariso n o fp an d C NAA Q S ) ar e bot h betwee n 1 0 an d 9 0 percent
. I ti s thu s a n interva l withi n whic h th e determinatio n o fattainmen tstatu s i s unreliable

. W

e ma ytherefor e us e critica l interval s t o judg e th e adequacy o fcompletenes s requirements—th e more stringen tth e requirement , th e narrowe rth e critica l interval

. Th e critica l interva l fo ra sit e recordin g onl yfiv e vali d lea d sample s pe rquarte ri s 1.1 3 t o 2.2 3 pg/n r(2 5 percen tbelo wt o 4 9 percen tabov e

• 3 th e standar do f 1. 5 pg/ m ) , wherea s th e interva l narrow s t o 1.2 4 t o 1.9 0 pg/n r (1 7 percen t below t o 2 7 percen t abov e th e standard ) fo r a sit e tha t record s 1 2 vali dsample s pe r quarter . Figure s 3- 1 and3- 2 suppor t simila r conclusion s regardin gTS P long-ter m standards . Sinc e long-term averag e concentration s ar e wel l below th e standar dat mos t sites , th e benefit s o f 1 2 versu s fiv e sample s pe r quarte r ar e cleare r fro

m th e curve s depictin gpossibl e misclassificatio n probabilitie s tha nb y a direc t compariso no f site s wit hdifferen t propor tion s o f dat a completeness . W e conside r tw

o form s o f annua l TS P standard , bot hhavin ga referenc e concentratio no f 7 5 pg/ m , namely , th e curren t standar dbase donth e geometri c mea nanda hypothetica l standar dbase don anarithmeti c mea n(the latte r i s relevan t t o th e recentl y propose dPM-1 0 standard) . Fo r th e curren t standard , th e critica l interva l fo r th e cas e o f fiv e vali dsample s pe r quarte r i s 6 7 t o 8 5 pg/m 3 (1 1 percen t below t o 1 3 percen t abov e th e standard) , whic hnarrow s t o 7 0 t o 8 1 pg/ m ( 7 percen t below t o 8 percen t abov e th e standard ) fo r th e cas e o f 1 2 vali dsample s pe r quarter . Thes e result s ar e th e same (t o two-digi t accuracy ) fo r th e hypothetica l standar dconcentratio nrepresente dinFigur e 3-3 .

I t i s instructiv e t o compar e th e case s o f lea dan dTS P represente db y Figure s 3- 1 an

d3-2 , respectively . Th e probabilit y o f misclassificatio n i s determine dno t solel y b y th e proportio no f dat a completeness , bu t als o
83146 r io 4 1P.6 9

b yth e numbe ro fvali d observation s t o be use d i n judgin g attainment

. Fo rexample , 5. 5 percen tcomplet e dat a yield s 2 0 observation s wit h whic h t o judg e attainmen to fa n annua l standard , bu tonl yfiv e observation s fo rassessin g th e attainmen to fa quarterl ystandard

. Therefore , a give n numbe ro flea d sample s pe rquarte rwit h whic h t o judg e attainmen to fth e quarterl ylea d standar d lead s t o highe r misclassificatio n probabilitie s tha n th e same numbe ro fTSP sample s pe r quarte r(i.e. , fou rtime s tha tnumbe rpe ryear ) wit h whic h t o judg e attainmen to fth e annua l TSP standard , othe rfactor s bein g equal

. Thus , th e critica l interva l correspondin g t o th e 1 2 lea d sample s pe rquarte r require d fo rattainmen tdetermination s i s relativel ywide r(1 7 percen t belo wt o 26 percen tabov e th e quarterl ylea d standard ) tha n th e interva l correspondin g t o th e require d 5 TSP sample s pe rquarte r(1 0 percen tbelo w t o 1 3 percen tabov e th e annua l TSP standard)

. Therefore , i fou rgoa l wer e t o contai n th e 0.1 0 probabilit ycritica l interva l within , say , + 1 0 percen to fth e standard , the n th e requiremen t o ffiv e TSP sample s pe rquarte rnearl yachieve s th e goa l wherea s th e requiremen to f1 2 lea d sample s pe rquarte rdoe s not
. Thi s suggest s tha t an ytightenin g o fcurren tdat a completenes s requirement s t o improv e attainmen tassessment s o flong-ter m standard s shoul d begi n wit h lead

. O n th e othe rhand , i fou rgoa l wer e t o restric tth e fractio n o fsite s vulnerabl e t o misclassification , e.g. , site s fo rwhic h th e misclassifica
tio n probabilit yexceed s 0.10 , i tappear s fro m th e 198 1 dat a tha tan y tightenin g o fcompletenes s requirement s shoul d be directe d a tTSP rathe r tha n lead , sinc e th e observe d fractio n o f"vulnerable " site s i s greate r fo rTSP tha n fo rlead

. Thi s differenc e betwee n TSP an d lea d ma ybe solel y a consequenc e o fth e smalle rsampl e o flea d sites , however

. Th e two goal s thu s lea d t o differen tconclusion s abou twhic h pollutant , lea d o rTSP , warrant s th e mos tconsideratio n fo rstrengthene d dat a completenes s requirements

. Sinc e th e "critica l interval " analysi s seem s t o be more reliabl e tha n th e "vulnerabl e sites " analysis , i tseem s tha t requirement s fo rlea d warran tfirs tconsideration

. Thi s i s especiall ys o sinc e strengthe d requirement s fo rparticulate s hav e alread ybee n 4 2 83146 ri o **•P.7 0

considered ; propose d revision s t o th e particulat e NAAQ S includ e a requiremen to f1 2 vali d observation s pe rquarte r(EPA , 1984)

. Th e recommendatio n tha tstrengthene d completenes s requirement s b e con sidere d fo rlea d i s offere d onl yas a poin to fdeparture , however , sinc e realisti crecommendation s mus tinclud e man ypractica l consideration s outsid e th e scop e o fou ranalysis

. Fo rexample , w e hav e no tattempte d t o distinguis h i n genera l betwee n dat a completenes s proportion s resultin g fro m goo d dat a recover yfro m intermitten tsampling , o n th e on e hand , versu s poo rdat a recover yfro m continuou s samplin g o n th e other

. Th e latte rcas e ma

yb e indicativ e o fgenerall yunreliabl e data

. Pendin g a more comprehensiv e revie wo fdat a completenes s requirements , however , th e result s presente d i n Tabl e 3- 1 an d Figure s 3- 1 throug h 3- 3 shoul d b e usefu l becaus e the ysho wtha tcurren trequirement s ar e adequat e t o rende r reliabl e determination s o fth e attainmen to flong-ter m standard s a tal l bu ta smal l fractio n o fsites

.

SHORT-TER

M AMBIEN

TSTANDARD

S
Correctl yclassifyin g th e short-ter m attainmen tstatu s o fa sit e havin g a certai n dat a completenes s proportio n i s muc h more difficul ttha n classify
- in g th e long-ter m attainmen tstatus
. Tha tis , th e estimate d long-ter m averag e i s a more stabl e statisti ctha n th e observe d numbe ro fshort-ter m exceedance s o rth e estimate d tota l numbe ro fexceedances

. Becaus e o f this , i ti s no tpossibl e t o reliabl yestimat e th e chanc e o fmisclassifyin g th e statu s o fmos tsites

. W e can , however , estimat e th e averag e chanc e o f misclassifyin g site s whos e proportio n o fdat a completenes s meet s a require d proportion

. Thi s ca n be don e i fw e mak e th e reasonabl e assumptio n tha tth e actua l numbe ro fshort-ter m exceedance s (x. ) an d th e dat a completenes s proportio n (p.j ) ar e statisticall yindependent

. Tha tis , w e ma yus e a n estimat e o f th e tota l numbe ro fexceedance s a teac h sit e t o estimat e th e statistica l distributio n o fshort-ter m exceedance s acros s al l sites

. W e the n appl y 83146 r i o 4 3P.7 1



### thi s statistica l distributio nt o thos e site s havin gdat a completenes s proportions , p^ , nea r a give nproportion , p , an dthereby

estimat e th e fractio no f thes e site s havin ga give nnumbe r o f exceedance s ( x= 0 , 1 , 2 , ...) . Fo r a one-exceedanc e standar d(fo r whic hr = 1 in th e notatio no f Sectio n2) , we may therefor e estimat e th e probabilit y o f misclassifie dattainmen t an dnonattainmen t sites , respectively , a an d3 , associate dwit ha give nproportio no f dat a completeness . Th e estimate s o f a „ an d3 „ ar e P P

a = Pr(x . > 1.0 5 | p . = p , x i 

### = 1 ) Pr(x i 

### = 1 | X i 

### < 1 )

= p.Pr(x i 

### = 1 | X i 

### < 1) , i f p<y-^ -

= 

## ° . if 

## P>-O F
an d ma

x 

## 3 p

## = £ Pr 

## (* i < l



## - 05



## I P i = 

## P » x 

## i = x 

## ) pr 

## ( x 

## i = x



## U j > 2 

## )

X 

### ^ x

### \

- 1
= E d" P ) Pr(X i 

### = X| X i 

### > 2) , i f p< T 

### - i F AC

m a x> 1 * 1


### = L C d- P) X

### + xp( l - p)*" 1 

### ] Pr(x j 

### = X| X. > 2) , i f p> y- ^
A— £ 

### W e the nestimat e a(p Q 

### ) and3(p Q 

### ) a s

a(p 0 

### ) = averag e a p

### , P i 

### > P 0 

### 3(p 0 

### ) = averag e 3 p

### , P i 

### > Pg

I nestimatin gth e statistica l distributio no f th e tota l numbe r o f exceed ances , instea do f usin gX- = k./p . we us e X- , obtaine db y fittin gan

83146 T 1 0 4 4P.7 2

exponentia l distributio nt o th e uppe r 1 0 percen t o f th e distributio no f concentration s a t a sit e (the exponentia l tai l i s fitte dusin gavailabl e statistics—th e 90t hpercentil e an dth e maximu

m concentration) . Thi s i s don e t o avoi da certai nlumpines s inth e distributio no f x , (e.g. , th e minimu m positiv e estimat e obtaine dfro m a onc e pe r si xday samplin g schedul e i s 6) , an

dresult s ina large r estimat e insevera l cases . Th e formul a fo r xi s

x=k+(N-n + 1)( 1 - y ) exp 

## ' C

## NAAQ S " X 

## Al n x m , „ - x/ ma xyI

wher e we hav e suppresse dth e inde xi fo r eas e o f notatio nandwher e
k= th e numbe r o f recorde dexceedance s N = th e numbe r o f observation s ina complet e dat a se t n= th e numbe r o f recorde dconcentration s
• y = 0.9 , correspondin gt o th e us e o f th e uppe r 1 0 percen t o f th e concentratio ndistributio n x= xg # g , th e 90t hpercentil e o f th e concentratio ndistributio n
x ma x= t ' 1 e max i m u m

recorde dconcentratio n n= n/( n+ 1 )
Provisio ni s mad e i nth e calculatio no f xfo r case s inwhic heithe r x> ^NAAO S o rxa= x. I nthes e case s xwa s se t equa l t o x= k/p . It shoul dals o be note dtha t Xi s rounde dt o th e neares t intege r t o estimat e th e statistica l distributio no f th e tota l numbe r o f exceedance s amon gal l sites .
Th e result s o f thes e calculation s ar e show ninFigure s 3- 4 throug h3- 8 an d inTabl e 3-2 . The figure s indicat e th e chanc e a tha t a sit e randoml y selecte dfro

m a grou po f attainmen t site s havin gdat a completenes s proportio npwoul db e misclassifie da s nonattainment , o r th e chanc e 3 tha t a nonattainmen t sit e woul db e misclassified . I ncontras t t o th e mos t sensitiv e scenario s treate di nSectio n2 , Figure s 3- 4 throug h3- 8 indicat e
83146 T 1 0 

### 4 5P.7 3



### (a ) a versu s p

0.0 6 1

-

0.06

- a. 0.04 r

0.02

-

0. C



### (b ) 3 versu s p

~ > r

FIGUR E 3-4 . Estimate dprobabilitie s a an d3 o f misjudgingattainmen t o f the 24-hou r sulfu r dioxid e standar da s function s o f dat a completenes s proportion , p . 8314 6 

## 4 6P.7 4



### (a ) a versu s p

O.O B

0.06 r X I £ 0.0 4

0.0 2 -

O. C

0.0 6

0.0 6

J D 0.0 4

0.0 2

0 . O C 

### (b ) 3 versu s p

- i 1 1 j 1 1 1 r

1 ' ' 0 0. 2 0. 4 0. 6 O. B 1. 0 P


### FIGUR E 3-5 . Estimate dprobabilitie s a an d3 o f misjudgin gattainmen t o f the one-hou r carbo nmonoxid e standar da s function s o f dat a completenes s proportion , p .

8314 6 

## 4 7

### (a ) a versu s p P.7 5

0.0 8 - i 1 1 1 r " i 1 r

0.06 - Q 0

. 0 4

0.02 -



### (b ) 3 versu s p

0.0 8 -

0.06 r
0) 0.0 4 -

0.02 L

0. 0



### FIGUR E 3-6 . Estimate dprobabilitie s a an d3 o f misjudgin gth e 8-hou r carbo nmonoxid e standar da s function s o f th e dat a completnes s proportion , p .

8314 6 

## 4 8P.7 6



### (a ) a versu s p 1 . 0 i 1 1 1 1 1 1 r

0. 8

0. 6

. c Q . 0. 4

0. 2 -



### (b ) gversu s p

Q J X ) 1 . u
0. 8

0. 6

0. 4

0. 2

n n 1 ' ' ' '

- \

i 1 i 1 i | i | i

-

-

-

7 1 - H — J !_- . 0. 0 0. 2 0. 4 0. 6 0. 8 1. 0 P


### FIGUR E 3-7 . Estimate dprobabilitie s a an d3 o f misjudgin gattainmen t o f th e maximu m dail y one-hou r ozon e standar da s function s o f th e dat a completenes s proportion , p .

8314 6 

## 4 9

### (a ) a versu s p P.7 7

0.0 8 -

0.0 6 o sz a 0.0 4

0.0 2

0. 0 -i 1 r "1 1 1 r

0.0 8

0.0 6 o V XJ 0.0 4

0.0 2

0. 0 

## 8. o

FIGUR E 3-8 . Estimate dprobabilitie s a an d3 o f misjudgin gattainmen t o f the 24-hou r TS P standar da s function s o f th e dat a completenes s proportion , p .

8314 6 

## 5 0P.7 8



### TABL E 3-2 . Estimate dprobabilit y tha t a site' s attainmen t statu s fo r short ter m standard s i s misclassifie da s a functio no f minimu m dat a completenes s pro portion , p 0 

### . Th

e tabl e entrie s ar e th e estimate dpercentage s o f attain men t site s (A ) meetin ga give ndat a completenes s requirement , pg , likel y t o be misjudge da s nonattainment , o r th e percentag e o f nonattainmen t site s (N ) likel y t o be misjudge da s attainment . N o estimat e i s mad e i f th e numbe r o f site s i s insufficient . (Note : Ther e ar e no know nspecifi c instance s o f misclassifie dsites—se e tex t fo r a discussio no f th e estimatio nprocedure. )
S0 2 (2 4 hours) : 89 1 site s Assigne d Statu s

Actua l p 0 = .5 0 (85%) ap Q = .7 5 (71% ) p 0 = .95 b (8% ) Statu s A N A N A N A (98%) c2% d2 % 0 % N(1% ) C O ( 1 hour) : 48 0 site s

Actua l p 0 = .5 0 (79% ) p 0 = .7 5 (65% ) p 0 = .9 5 (22% ) Statu s A N A N A N A (99% ) < 1 % < 1 % 0 % N(1% ) C O ( 8 hours) : 48 0 site s

Actua l p g = .5 0 (79% ) p 0 = .7 5 (65% ) p 0 = .9 5 (26% ) Statu s A N A N A N A (57% ) 2 % 2 % 0 % N(43% ) < l% e< 1 % < 1 % 

### O 3 (Dail y

maximu m 1-hour) : 72 3 site s Actua l p 0 

### = .5 0 (86% ) p Q

### = .7 5 (70% ) p Q

### = .9 5 (16% ) 

## Statu s A N

A N

A N A (63% ) 8 % 8 % 0 % N (37% ) < 1 % < 1 % < 1 % Continue d

83146 T 8 b lP.7 9

TABL E 3-2 . (Concluded )
TSP (24-hours) : 366 7 site s Assigne d Statu s

Actua l Statu s A(79% ) N(21% ) p 0 = .0 5 (92%) f

AN 2 % 6 % P

g = .1 2 (77% ) AN 2 % 6 % p 0 = .2 5 (6% )

AN 3 % 4 %

a Th e numbe r i

n parenthese s i s th e percentag e o f site s meeting the required dat a completenes s proportion , PQ . Unde r curren t procedure s fo r determining the attainmen t o f short-ter mstandards , thi s completenes s requiremen t rule s ou t the possibilityo f a fals e nonattainmen t determination
. bB y0.9 5 the valu e y ^ = 0.952 4 i s meant .

c The numbe r i

n parenthese s i s the estimate d percentag e o f site s tha t have attained o r failed t o attai n th e standard . Thes e estimate d percentage s ar e based o n th e tail-exponentia l metho d discusse d i n the text . dThi s i s the estimate d a-probabilitytha t a sit e i n attainmen t an

d meeting th e dat a completenes s proportion p g = 0. 5 woul d be misjudge d a s nonattainment
. Thi s entr ycan als o be interprete d a s a n estimat e o f th e percentag e o f suchsite s expected t o be misclassified
.

e Thi s i s th e estimate d 3-probabilitytha t a sit e no t in attainmen t o f th e standard an

d meeting dat a completenes s proportio n p g = 0. 5 woul d be misjudge d a s attainment
. Alternatively , we ca n interpre t thi s entr ya estimat e o f th e percentag e o f suchsite s expecte d t o be misclassified
.

' Not e tha t intermitten t monitoring i n itsel f result s i

n les s tha

n complet e data . Th

e dat a completenes s requirement s o f 0.05 , 0.12 , an d 0.2 5 correspon

d approximatel yt o five recorded 24-hour measurement s pe r quarter , an d 7 5 percen t dat a recover yfro monce-per-six-da

yan d once-per-three-da ysampling schedules , respectively
. a s a n

83146 r 8 5 2P.8 0

tha t a and3 ar e fo r th e mos t par t wel l below 1 0 percent . Thi s i s becaus e mos t site s ar e estimate dt o have no exceedances , inwhic hcase ther e i s no chanc e o f misclassification , an dthos e site s estimate dt o have mor e tha non e exceedanc e frequentl y appea r t o have mor e tha ntwo exceed ances . I

nshort , th e mos t difficul t case s treate dinSectio n2 appea r t o b e rare inactuality . I t shoul db e note dtha t th e dat a ar e fro m 1981 , s o that , fo r purpose s o f comparison , ozon e i s treate da s havin gth e same one yea r one-exceedanc e form o f standar da s th e othe r pollutants .
Tabl e 3- 2 show s a

napproximat e 1 o r 2 percen t probabilit y o f misclassi fyin gsulfu r dioxid e andcarbo nmonoxid e sites . The a - an d3-probabilitie s o f misclassificatio nar e estimate dt o be 8 percen t an dles s tha n1 percent , respectively , fo r ozon e sites . Fo r particulat e site s a an d3 ar e estimate dt o b e 2 an d6 percent , respectively .

SUMMARY
The 198 1 dat a indicat e tha t unde r curren t dat a completenes s requirements , th e rat e o f misjudgin gth e attainmen t o f eithe r long-term o r short-term standard s i s unde r 1 0 percent , andi nsevera l instance s th e estimate d misclassificatio nrat e i s unde r on e percent . Th e curren t completenes s requirement s therefor e seem t o b e adequat e fo r generall y reliabl e attain men t determinations . Thi s conclusio nshoul dbe regarde da s tentative , however , pendin gdevelopmen t o f specifi c objective s fo r limitin grate s o f misclassification .
We hav e als o conducte dwha t mus t be regarde da s a preliminar y analysi s o f th e benefit s o f strengthene drequirement s fo r dat a completeness . In general , direc t estimate s o f th e expecte dpercentage s o f misclassifie d site s reveal s virtuall y no chang e resultin gfro m th e change s inrequire ment s tha t we consider . Takin ga les s direc t approac hinth e cas e o f long-term

standards , however , we hav e estimate da critica l interva l abou t th e standard , withi nwhic hmisclassificatio nprobabilitie s excee d1 0 percent , an

dwe hav e show

nho w thes e critica l interval s narrow a s mor e

831l+6 r 1 0 5 3P.8 1

complet e dat a ar e required . The analysi s suggest s tha t strengthene d requirement s be considere dfo r leadan dpossibl y

fo r particulate s (the recentl y

propose drevision s t o th e particulat e standard s includ e suc ha strengthene drequirement) . A simila r analysi s i s possibl e fo r short-term standards , althoug hth e analysi s woul drequir e some assumption s abou t th e frequenc y

distributio no f concentration s a t individua l sites .
I nan y case , realisti c recommendation s fo r strengthene ddat a completenes s requirement s mus t addres s th e broade r issue s concernin gdat a reliabilit y andcost-effectiv e ai r qualit y management . Fo r example , w e shoul d probabl y

conside r th e benefit s o f strengthene dqualit y assuranc e proce dure s alon gwit hstrengthene ddat a completenes s requirements . Similarl y we migh t conside r whethe r mor e accurat e attainmen t determination s migh t b e achieve dunde r curren t dat a completenes s requirement s b y changin gth e form o f curren t short-term standards . W

e hav e no t attempte dt o includ e suc h issue s inth e presen t analysis , althoug hthes e issue s certainl y deserv e futur e consideration .

83146 T 1 0 

### 54

### STATISTICA L ESTIMATIO NO F NATIONA L AI RQUALIT YSTATU S AND TREND S

W e no wconside r th e estimatio no f nationa l average s o f site-specifi c annua l averages , p. , an dsite-specifi c exceedanc e rates , x.. . W e fac e th e choic e o f a minimumdat a completenes s proportion , pg , suc htha t onl ythos e site s i nou r sampl e wit hp . > p n

### wil l b e use dt o estimat e th e nationa l average .

AQUESTIO NO F BIA S
Le t 1 v( 

### P? > . P(PQ ) = v 

### (Pg ) E v.



## an d v(Pg ) X(Pg ) = v(p 0 

### ) E x 

### i
wher e p . an dx . ar e estimate s o f th e site-specifi c long-ter maverage , \i-, an dexceedanc e rate , X- , respectively , andwher e th e summatio ni s ove r site s i nth e sampl e wit hp . > p Q 

### . I f w e assum e tha t fro msit e t o sit e th e ai r qualit yparameter s p i 

### an dx . ar e independen t o f th e proportio np ^ o f dat a completeness , andi f w e regar dth e se t a o f site s i nou r sampl e a s havin g bee nrandoml ydrawnfro mth e populatio no f site s o f interest , the n fo r eac hp 0 

### , p(pg ) andx(p Q 

### ) shoul db e unbiased , i.e. , w e shoul dhav e

A 

### E y(Pg ) B 

### u = E(u.j )

8314 6 4 5 5P.8 3

an d

A E x(p Q ) = X= E (x ^ .

AA Thi s lac

ko f bia s follow

s fro m th e unbiasednes s o

f p i an dx. ,
E p n - = Pi 

# EI. = X i

as discusse

di nSectio

n2 .

AA Under thes e assumptions , p(p Q ) an dx(p Q ) ar

e approximately

normally distribute

dunbiase

destimator s of pan dx , respectively . Thus , a natura l criterio

nto us e i

nselectin

g p g i s th e variance o

f thes e estimators .

VARIANC E O F TH E ESTIMATORS

AA I nChapter 2 w

e obtaine

dexpressions fo

r th e variance o

f Pi an dx i 5 conditiona l o

nth e choice o

f site , i , an

dthus conditiona l o np. , x- , an

dp . Extendin

g th e conditiona l variance to th e average s take

nove r th e se t ( / of site s i

nou r sample , w

e hav e v(Pn ) i
_„ 2 

## Var(p(p 0 

## ) | JI ) *- | E 9' 1 - P

« o i l v fc (pn ) 1= 1 p i



### an d

1 V( ' o)l - P i

Va r (x(p 0 ) | J/ ) i- 1 E - T r L x 1

v (p Q ) 1= 1 K l

8314 6 4 

### 5 6P.8 4

T o obtai n th e unconditiona l variance , w e mus tallo wfo rsite-to-sit e difference s i n P i an d x. . Thu s (see , fo rexample , Brillinger , 1966 )

Va r(P( P Q ) ) = E {va r(A 9Q ) | ^) } + Var{E(p(p Q ) | &)}

v (Pn ) i

. n J-

\ i

v (Pn ) 

## i

_V 1 

## " P 

## i ° 1 2, , H, D , N 

## * ^{-f 2 J -^— L 

## -^> + Va r v(p Q ) i= l

whic h ma

ybe estimate d as
v( 

### P0 > 1 - p . 2

, »<P0

### > 

## Va r(;(p 0 

## » --j-J - E -j-l-J * 1 E (.,-;(P 0 

## )) 2

v(p Q ) 1= 1 K l v(p Q ) 1= 1 Similarly , w

e hav e

X(p 0 } 1 - p . \ I

, v(p 0 } ( i HQ /

1 " P

H ) ( i ^

whic h ma

ybe estimate d as

u ( P[) ' 1 - p . , v( Po > 

## Ja rU( p ) . "L

- E —J-x , + 

## -jX yE (X , -lip,,)) 1 V(Pn ) 1= 1 r l V r 0 ' 1= 1 •(P 0 )
Th e varianc e expression s ar e simila rfo rp(p Q ) an d x(p Q )

. I n bot h cases , th e secon d ter m ma

ybe expecte d t o be proportiona l t o l/v(p Q ) whil e th e firs tter m depend s o n th e distributio n o fP i (a s p 0 increase s t o unity , ( 1 - Pi)/P i approache s zer o bu tl/v(p Q ) increases)

.

8314 6 4 57

### A N EMPIRICAL ASSESSMEN T O F TH E DATA

COMPLETENES S NEEDE DT ORELIABL YESTIMAT E NATIONAL TREND S

SULFURDIOXID E

A 

### Tabl e 5- 1 andFigur e 5- 1 show p(p Q 

### ) , th e estimate dcomposit e annua l averag e sulfu r dioxid e (SO2 ) concentration , an dVa r (p(p 0 

### )) , it s estimate d varianc e a s discusse dinSectio n4 , fo r severa l value s o f require ddat a A 

### completenes s proportio npg . Th e estimato r p(p n 

### ) i s fairl y constan t wit h Pg , bu t it s estimate dvarianc e increase s substantiall y fo r p Q

### > 8 0 per cent , consisten t wit hth e discussio ninSectio n4 . Similarly , Tabl e 5- 2 andFigur e 5- 2 sho w a substantia l increas e inth e A 

### estimate dvarianc e o f x(p Q 

### ) fo r p Q

### > 8 0 percent . I naddition , Figur e 5-2 a suggest s tha t increasin gth e require dpercentage , pg , may induc e a positiv e bia s inth e estimat e o f th e nationa l averag e exceedanc e rate . A 

### Th e increas e inx(p Q 

### ) accelerate s fo r p g> 8 0 percent . I nsummary , i t appear s tha t no mor e tha na n8 0 percen t dat a completenes s requiremen t shoul dbe usedt o estimat e th e nationa l statu s an dtrendi n SO 2 concentrations .

CARBON MONOXID E

A 

### Tabl e 5- 3 an

dFigur e 5- 3 sho

w th e estimate dexceedanc e rate , x(p n 

### ) , an d AAU 

### it s estimate dvariance , Va r (x(p Q 

### )) , fo r hourl y average dcarbo nmonoxid e (CO ) concentrations . Interestingly , site s wit hmor e complet e dat a hav e no AAA 

### observe dexceedances , s o tha t bot hx(p Q 

### ) an dVa r (x(p Q 

### ) ) ar e calculate d t o be zer o fo r pg> 9 4 percent . Fo r smalle r value s o f pg , however , bot h
AAA 

### x(p 0 

### ) and Va r(x(p Q 

### ) ) increas e wit h p 0 

### , wit h a fairl yshar p ris e i n 8314 6 9 5 8•c

C D

O l v o rOtOU>>t*Ulcri<Tl^I^J^IOOOOOOCOtCV0lOVOVOl 0 OiP.HVDvlWIOHvlO^OONJUlvlCOOHWiO.UlCf t WC0Uia)C0COCDHvl>lUvJ|O|0tONlU1>lOt0r00 0 oooovovooovovovovoovoovoovooovov o OOOOVOVOOOVOVOVO^OOVOOVOOVOOOVOV O OOOOVO<£>OOVOW>U>VOOVOOVOOVOOOVOV O OOOOUD^OOOVOVOVOVOOVOOVOOVOOOVOU D lOtOrOrOIOtOloiorotOrOrOrOrOrOtOtOtotOrOrOt o uitniji^itkLn(jiLnLncriLnLn0^cyt-~j-j~JO>^j^jcT>v o OOIOvJCOSJWCnvlO>COVDHO\HrOif>COOiti>vDJ > N)WkOvJ(DPOvlCh«)HvlU1tOWO\vO-JVDvJCO* > <Tivo^vo**co<Ticncx)U)votjiNJOCoi-'aivoJ^i- , *kC n OOOOOOOOOOOOOOOOOOOOO O OOOOOOOOOOOOOOOOOOOOO O OOOOOOOOOOOOOOOMI-'l-'rOrOie»lj J wwwoJwwwJ>^uiuio\-ja3«JtoJ>uiMrowK ) HHWK>WU100MmomHMtOCOONlWH«)vi m HvnOIOOK)W(J\00UiO«)WOCh-J>J(j\wWU W OWNJ00KJ--JUIVH00\C0C000VIC0VJUJOHOHU I WOOsJOOiDlOIOHUIMNlOHUHDOM-'rJHW-J H - o o

• £> C D ^-" '

< >

- J
-a o —* *

— ' T

ro J D c w .

- j ro a . T 3 ro

- s o ro 3 r + D J C O ro > «

• a o o

- h a . O J Q J O Ompl ro C +eness. < D J

- s O J 3 O ft ) o

- h o 3 ro i 3 " v . O c Q J

-s -e >

- a o v. , O J
- t i 3 n c +ons o

- h c + ZT ro 

### 5 5 r o • *

• c > *— .. " O o
O l 3 a . - J

. r + Vt ro <- +ima c- + ro Q . — 1 i > 0 0 1 — m c n i •
m Vt r + - j

. = 1 O J r + rodcom

" O o vt —1. ro
3 Sual O Jverage(A ) P(P Q ) versu s p Q (B ) Var(p(p Q ) ) versu s p f

- + + + + ++ + + +

+ + + + + + + V + +

CT ! O e C U

• o C U C O E •r - -t-> VI

°b . c u o

- o O ) +- > E

ie . 20 . 30
. 10
. 53
. 60
. + +

• - + + + + + + ++ + j-H 70
. 80
. S3
. \Z%. °b . 10
. 20
. Require d Percen t 30
. 10
. 50
. 60
. 70
. Require d Percen t 80
. 90
. 100
.

FIGUR E 5-1

. Estimate d long-ter m SO 2 ai rqualit yparameter s as function s o fth e require d dat a completeness , p n .

R 1 1 4 6o tn i~ 3 o i «3 - CV J 4 - O C U +- > C O s - ^

_ CO 3 C C ro C ( O c u 1 = X3 C U +- > ro F • ^ + J t o L U

C M 1 I T ) L U

_ J C O e t H c u o r o •r — 4 - ( O >

- o O l + J ro E • r 4- > t/ > C U V) 4- > •r — T 3 C ro « . * - - o Q . ^— ^ < r<
C U o c ro • a c u < tl ) u X a i < i j - O J Q . T 3 Q J
• r 3 cr c uia> j = •t- >HO VI c o • r - +- > O C 3 <4 - V) r o

. .

*•—• * o a . *— ^ .- < ^-* - j - r o > .

i/ i t o c u c t u -t- > c u r — a . E o o ro + J < o • o M - O o Q . • » C U o> ( 0 +- > c c u u ^-^

s —^ o < <<Var

,,—. . o Q . < <<

o O . Hcoinoo') , fSvo(Nmu)voooi , ' ] , oor>cn>T H r>ooronor>Ofr> , s , incoC)CMncscriMn')'Mf o I'HI^OOrOMHCO^DinfMMtStNCNHHHHH H OinCNrHiHrHrHOOOOOOOOOOOOOO O CNOOOOOOOOOOOOOOOOOOOO O OOOOOOOOOOOOOOOOOOOOO O

OOOOOOOOOOOOOOOOOOOOO O OOOOOOOOOOOOOOOOOOOOO O ^coocNocNco^cnrHvovovocNcocNm^i'voinoor - cNCT><X)CT>r--ocT>LnrocNocTicx)cx)r^voLO'a'rorocNC N inCNCNCNCNrOCNCNCNCNCNiHi-HrHrHrHiHrHrHrHrHr H 

### ooooooooooooooooooooo o o<x>ocT\crioocr>cr><yicr>a>oooo<yi<xiooo o ocno(ncnooo\maicf)moooo(Mnooo o C0r~CNCX)^rrHl>-CNrHrHrHCNrHCDOr-t--Cnr~O00'q < orr»cninH*ri)cio\oor^Hi'Oocoin(nr>inr > CO^n^DOTI^HWHOOlfCDOintNCSHCHO N r-vDirincNooor~-^j , CNOoroovoorHror-OrH^' o cr»a>cT»a»cncT»oocooooor^r^r~vovoin'a'roro< N t£ >

(X ) J -- r 0 1 

### Z 9

Estimate dExceedanc e Rat e

Q J T l t/ ) I- H CD

- hc r C 73 3 m o rt - t n -< •i o r o 3 • Vt o m

ro O J

- 5 ro

- O e • J .

- s ro Q . Q . O J ( T D J O O ro Q . Vt 3 " o T r r r + ro T 3 o o o 3r\ > • o ^ J ro Cf ro 3 ro t o t o V

• a O • Q J ^ . 1

. Q C 0 1

— j ^ . rt - ^ <

• a Q l T Q J 3 ro rt - ro

- 5 t o J3. 0 0. 1

ro

- j o ro 3 r r 0. 2 I 0. 3

I 0. 1

I 0. 5 0. 6 0. 7 0. 8 0. 9 i 1. 0

ZO ro c

- s ro a .
• n .I s

c n ts» - +

+ + +

+ + + + O < ro

- j t o c t o • a o

."t f



### Estimate dVarianc e _0.0 0 0.0 5 0.1 0 0.1 5 0.2 0 0.2 5 0.3 0 0.3 5 0.1 C
siiii iii ii

73 ro

- O c ^ .
- s ro C L -o ro

- s o ro

- i rt - •

Q "

u i s r

c n C3 ~

- j E 3 "

C D ca "

U D -

- h

r> 4 -

-f - f 4 - + .1 5 0.5 E

Q>

• o O

< ro

- s t o c t o -a oXT

c n

•^toio < t*cncft^c»c»a>a>v0'0v0vovovovou>v£ > OOOvninUHOOiUH(Tia)U)OIOU)iMjimO)>JO O WUlOUOOOvlWtOOOOOUlv l OOOOOVOOOVOVDOVOVOV O ooooovooovovoovovov o OOOOOVOOOVDVDOVOVOV O OOOOOVOOOVOVDOVOVO^ O OOM(00»NI> J O^OOOVOVOOV O OVOOOVOVOOV O OVOOOVOVOOV O

C D O O OOOOOOOOOOOOOOOOOOOOO O OOCDOOOOOOOOOOOOOOOOOO O »mvlvnO>0\CTlNlv|NlCOUlUl»MHOOOOO O OOCOHNIOWNIMUOOUIM^UIOWOOOOO O OOOOOOOOOOOOOOOOOOOOO O OOOOOOOOOOOOOOOOOOOOO O

OOOOOOOOOOOOOOOOOOOOO O OOOOOOOOOOOOOOOOOOOOO O OOOOOOOOOOOOOOOOOOOOO O r-'MI-'Mr-'h-'MI-'MtOOJI-'tOtOOOOOOOO O WWUlrOWUl>J(DK)mo«)W«)MHOOOOO O oovou>U)~j*».*.ootnovJi-Jvr><Ti^cDOoooo o T J o

> - > •-—« » T 3 O %^ ^

< > 0 1

- I *—* » >- J > .--^ H T 3 O * * * — O

o 3 X J

_ J ro c + ro 3 ro t o t o • o

- h r + 3 " ro

-s ro

- O c _ J .

- $ ro a .

- a ro

- j o ro 3 r i a l t o ro * # T 3 O V o

- h C L O l ri a l ro t o rt - - J

. 3 Q ) r + ro a . < a i

- i —i. a i 3 O ft ) I *
« C o >

-s ^—^

• > > ^-*. T 3 O ^^ « » O l (/ >

- h C 3ctions o 3 ro i 3 T o c

- j o o ro X o ro ro Q L OJ 3 > O ro V > > ^ > ^—* • " O o ^. ^ * » Q J 3 Q L
^ . r r t o - H 3 * 0 0 r - m t n i 0 0

. m «/ > r + - j

. 3 O J rt ro a . 3 ro C D 3 O ) 3 3 C O l

— i

- 1 Q l r r ro O

- h

C O•c

- 

## w

Estimate dExceedanc e Rat e

Q l VI -h c 3 O r t - —i. O 3 t o O - t l r r 3 " ro

- j ro

- Q C ^
. - s ro Q . Q . Q J r r Q J O O 3

- a

— ^ ro r r ro 3 ro t o 1 0 > # -a o

. T l t—t J J C 73 m o n i C O

. m t o r t - —i. 3 QJ r t - ro a . o 3 ro i 3 " o c

- 5 o o Q l —*

. - J

. a c Q J

— J _ j

. r t - ^ = T 3 Q l

- 5 Q J 3 ro r r ro

- s VI 73 ro c —J

.
- J ro a.

• a ro

- 5 o ro 3 rt - ( S ~

l\ J

LL )

J i C S "

c n S "

C3 ~

ca i i i i i i , i i i

« + + + +

+ + + + • o O < ro

- $ t o c t o

- o o

\ yj

Estimat e Varianc e Ji. 0000.0000.0010.0010.0020.0020.0030.0030.0010.001 0 S Ji ' • ' I i I I I l

73 ro

- O iZ — J .

- s ro o . ro

- 5 o ro 3 C O

0 1

- 5

- o O < ro

- j to c to

• a o

I 6 dP.9 2

Va r (x(p Q ) ) beginnin gat p 0 = 6 0 percent . Th e result s sugges t tha t no mor e tha n6 5 percen t dat a completenes s i s neede dt o estimat e th e nationa l hourl y COexceedanc e rate .
Tabl e 5- 4 an

dFigur e 5- 4 sho

w result s fo r 8-hou r COexceedances . Bot hth e A estimate dmea nexceedanc e rate , x(p n ) , an dit s estimate dvariance , AA* J Va r (x(p Q ) ) sharpl y increas e fo r p ^ > 9 0 percent , suggestin gtha t n o mor e tha na 9 0 percen t dat a completenes s requiremen t shoul dbe use dt o estimat e th e nationa l 8-hou r COexceedanc e rate .

NITROGE N

DIOXID E

A Tabl e 5- 5 an

dFigur e 5- 5 sho

w p(p n ) , th e estimate dcomposit e annua l

^ AA averag e nitroge ndioxid e (NO2 ) concentration , an dVa r (p(p n )) , it s estimate dvariance , fo r severa l value s o f th e require ddat a completenes s A proportion , PQ . Th e estimato r P(PQ ) i s fairl y constan t wit hPQ , bu t it s estimate dvarianc e increase s substantiall y fo r P Q> 9 0 percent . Thi s suggest s tha t n

o mor e tha na 9 0 percen t dat a completenes s requiremen t shoul dbe use dt o estimat e th e nationa l statu s an dtrendinNO 2 concentrations .

OZON E
Tabl e 5- 6 an

dFigur e 5- 6 show

th e estimate dexceedanc e rate , x(p n ) , an d AA^ it s estimate dvariance , Va r (x(p n )) , fo r dail y maximum

1-hou r ozon e (Oo ) concentrations . Bot hx(p Q ) an dVa r (x(p Q ) ) increas e wit hPQ , wit ha fairl y shar pris e inVa r (x(p 0 ) ) beginnin gat P Q= 8 5 percent . Th e result s sugges t tha t no mor e tha na 9 0 percen t dat a completenes s requiremen t shoul dbe use dt o estimat e th e nationa l dail y maximu m ozon e exceedanc e rate .

83Ht 6 9 6 5••••

C O C O

c n

CTt (Tt ••••

KJMU)iU(TlO>^JCOCOCOVOVOlDVOVOVOVOVOVOt O OOOU1U1iM-'00i».O(n00OPIV)U)U1UltTl>J00 O MinOUItOUlUitiiHGOtOUIHH^^OvJWc^^J U UX-'frCOOMDfrOUJ-rlM^vl^lCOUKTl^lOHCO O vJ^ONNtOVO^MMWCOvOOUlUIUxlljliJIitkVOU ) OOOOVOOlOVOV0OlOV0V0ViDOOVOlOV0OvOV 0 OOOOVDOVDVOVJDOVOVOVDVDOOVO^DVC'OVCIV D WWUIOWIOUJIOMWUJIjOLOUUUIUIUibfriUf e MIOWWOIOOHeOHHWUlWfcfcWUHDvl W CO<fiOnC*On^LiJ.UVO.I*<jOr-'CJ\MNJMOtOO\\OVO-« J 0\WHOOVOU10IO>JiI).aim«)vl(X)UlHa)vOmHL n tOHK)UllOit>WWr J UlUl|000-J J A(TllOWWK)U l OOVOOOOOOOOOOVOOVOOOOVOOVO O OOVOOOOOOOOOOVOOVOOOOVOOVO O
M M M lOlolOHHHIOrOIOrOMrOUUIU^^miOUlOOi n J>i(><TlWOO«3HJ>OrOUlvJWCOUlOUlWtOLnW* . HtOH^OO>U)(JOUJOHvlK)CO«kOH»IOOa) H WWWLnjiLrivlCflVDUHOMOi^JXjJWmOOOH W COiD>J>it>kOUl(OPi l >l- , cnvJtOMvD^]K}(TlUiUOI- ' Wvl(J)U100OUH0N)lv)a)W00H-«J(JlOM*O*'>D W v o J >muiocni-'o\i- , v]v4o-jii> j *w-sioa\Hviii i T 3 o

> ^ >

• a o *—^

< > O l T > " >

'•" ^ X J o •

r > o O -t , 3

- O r +

— •3 - ro ro < T ro - jequirness. ro a . T J ro

- s 0enta t a ro 1 *

• a 0 *Dfdata ro 0 0

t o 1 rt - 3 " - J . O 3 c O i - s cf ro 0 o . 0 < ro O ) X

- j 0 1 . n > o > ro 3 Q . 0 0 1 ro 3 < * 0 ro < > " O J

- 5 > - > >- •>T J •— » O
" O — » 0

» * ' 0 1 3 O J Q Lits5functions ••

- H 3 » 0 0 r - m o n

m t o < T 3 0 1 rt ro 0 . 3 ro 0 1 3 Q l 3 3 £ O J1rateof

T J C O c o

### 19

C O L O

Estimate dExceedanc e Rat e

Q J I O

- h c 3 o rt - _ J » O 3 t o T |
»— 1 (7 5 C 3 3 m o n i

- &

. o m

ro Q J rt -

- 5 ro roo . c o o - > •i

- 5 3 " ro o o . c

- 5 CL. a> c - )rtO Q J Q J o-> • o- j 3 TJXJ

— < c ro QJ C T— ' ro -< • 3 rt - ro<< to t o T J w Q l

- 5 T JQ l O 3 •ro cf ro

- 5 t o 73 rt> JZ> C J ^ . - 5 ro o .

- o ro

-s o ro 3 rt - J

(JL ) s - * *£> S "

L PSjJ

c n

C D

s r 10 . 20 . t 30 . T

+ +
+ +

++ + +
^ 10 .

+ + + 5 0

+ + T J

O < ro

- s t o c t o T J O

50 .

73 ro X J c - j

. ro Q .

- a ro o ro 3 cf U J E J
-

.x s

c n ea -

rn

~ j + +

+ + Estimate dVarianc e

100 . 150 . 200 . 250 . i 300 .

OT

O l -i
T 3 O

< ro

- j to C t o -a 

### o \

W dP.9 5

•

•

•

•

•

•

•

•

•

•

8 3 14 € • TABL E 5-5 . Es t o f one-hou r N0 2 variance , Var( 0 requ i re d perce n

p 

### 0 96.5000 0 95.0999 9 93.7999 9 92.3999 9 90.5000 0 88.2000 0 85.7000 0 82.0000 0 78.2000 0 75.2000 0 69.7000 0 65.5000 0 58.3999 9 51.3999 9 45.3000 0 37.1000 0 31.8000 0 26.9000 0 23.1000 0 17.5000 0 0.3000 0 0.3000 0 imated composit e annua l averag e

, P(PQ) , an

d it s estimate d ^p n )) , a s function s o f the u tage , p Q , o f 

# I(P 0 

# ) 33.4409 9 34.7530 0 41.2150 0 44.6939 9 44.6000 0 44.8960 0 44.3609 9 44.7140 0 45.9279 9 45.6250 0 44.9470 0 44.6349 9 45.0140 0 44.4539 9 43.9920 0 44.3019 9 44.2339 9 43.6550 0 43.7759 9 43.5640 0 43.3670 0 43.2469 9



### 6 8 dat a completeness . Var(y(p 0 ) ) 14.5987 8 6.8154 4 11.6274 3 10.4651 9 6.8971 1 5.3276 3 4.3596 4 3.4197 4 3.3488 0 3.1954 6 2.9265 6 2.7485 3 2.5641 0 2.4239 3 2.2454 1 2.2214 6 2.0557 8 1.9291 1 1 .7844 6 1.6498 2 1.5710 0 1.5757 5(A ) u(p 0 ) versu s pQ (B ) Var(p(p Q ) ) versu s p Q

+ +++ + + + + + + + + + ++-H
c n VD CT 3 O J ! T J O J +- > CO B, + +

10
. 20
. 30
. 10
. 50
. 60
. 70
. 80
. 90
. 100
. Require d Percen t c u - u c C O s _

T > ' Q J + J C O E + +

+ +

+ + +

+ + + + + + + +

10 . 20

- 30 . 10 . 50 . 60 . 70 . 80 . 90 . 100 .

Require d Percen t

FIGUR E 5-5 . Estimate d long-ter mN0 2 ai r qualit yparameter s a s function s o f th e require d dat a completeness , Pg .

83l't 6P.9 7

TABL E 5-6 . Estimate d mea n annua l rat e o f maximumdail yone-hou r ozon e exceedance , x(p„) , and it s estimate d variance , Var(x(p 0 )) : a s function s o f th e require d percentage , P Q , o f dat a completeness .

p 

## 0 98.8999 9 97.7999 9 96.3999 9 95.2999 9 93.8999 9 92.8999 9 91.5000 0 90.0999 9 88.2999 9 86.8999 9 85.0000 0 83.0000 0 80.2999 9 78.0000 0 73.7999 9 67.2000 0 61.2000 0 50.8999 9 40.0000 0 26.8000 0 3.7000 0 0.4000 0 ,

X(P 0 ) 12.8230 0 20.1000 0 19.9020 0 18.1840 0 15.0180 0 13.1060 0 12.8350 0 11.5830 0 10.6570 0 9.7190 0 9.1610 0 8.5340 0 8.1760 0 7.8380 0 7.4310 0 7.4520 0 7.3640 0 7.2000 0 7.7850 0 7.9380 0 7.9260 0 7.9150 0 " . A

Var(x(p Q ) ) 30.2039 9 22.9468 7 15.0890 7 10.6284 5 7.0156 8 5.0499 8 4.2744 2 3.3042 7 2.6744 9 2.1821 9 1.8207 0 1.5377 7 1.3261 4 1.1613 7 1.0147 5 1.0081 5 0.8977 9 0.8283 4 0.9316 9 0.9596 3 0.9664 6 0.9639 4

8314 6 

## 7 0(A ) x(p Q ) versu s p Q (B ) Var(A(p Q ) ) versu s p Q

O J -l- l rt J OC C U U c C O T 3 CD OJ O X L U T > < U +- > C O E •f+ +
+ + + + +

. + + +

1 0 . 20 . 30 . 10 . 50 . 60 . 7 0 . 80
. 90
. 100
.

Require d Percen t o c r a
co T J ' C U 4- J r o
+-> t o

• h + 1 0 . 2 0 . 1 30

. + 10

. + 5 0

. +

i 6 0

. /

7 0

. 80

. 90

. I0 0 Require d Percen t

FIGUR E 5-6 . Estimate d annua l ozon e ai r qualit yparameter s a s function s o f th e require d dat a completeness , pg .

8 3 I '• f )

u

### LEAD

Tabl e 5- 7 an

dFigur e 5- 7 show

p(p n 

### ) , th e estimate dcomposit e maximu m

*» » AA 

### quarterl y

lead(pb ) concentration , an dVa r (p(p n 

### )) , it s estimate dvari AA*• * 

### ance . Bot hp(p Q 

### ) an

dVa r (p(p Q 

### ) ) ar e fairl y constan t fo r p Q

### <

; 1 7 percen t (correspondin gt o complet e dat a recovery fro m a once-per-six-da y

samplin g schedule) . Onl y

a smal l numbe r o f site s hav e mor e tha n1 7 percen t dat a completeness . Th e consequen t ris e i nth e estimate dvarianc e suggest s tha t no mor e tha na 1 7 percen t dat a completenes s requiremen t shoul dbe use dt o estimat e th e nationa l statu s an dtrendinleadconcentration s unde r curren t samplin grequirements .

TOTAL SUSPENDE DPARTICULAT E

A 

### Tabl e 5- 8 an

dFigur e 5- 8 sho

w p(p Q 

### ) » th e estimate dcomposit e annua l averag e concentratio no f tota l suspende dparticulat e (TSP) , an dit s AAA 

### estimate dvariance , Va r (p(p 0 

### )) . Th e estimato r p(p fi 

### ) i s fairl y constan t wit hpg , an

dit s estimate dvarianc e show s no patternfo r increasin gpg . Therefore , i t i s no t possibl e t o judg e th e effec t tha t a nincrease ddat a A 

### completenes s requiremen t woul dhav e o nth e varianc e o f p(p 0 

### ) . Similarly , Tabl e 5- 9 an dFigur e 5- 9 show

no appreciabl e patter ni nth e
A 

### estimate dvarianc e o f x(p Q 

### ) . Figur e 5-9 , however , suggest s tha t increas ingth e require dpercentage , PQ , may induc e a positiv e bia s inth e estimat e o f th e nationa l averag e exceedanc e rate . Becaus e o f thi s possibl e bias , i t appear s tha t no mor e tha na 1 7 percen t dat a completenes s requiremen t shoul dbe usedt o estimat e th e nationa l statu s an dtrendi n TS P concentrations . SUMMARY
I ngeneral , we se e tha t th e advantage s o f includin ga larg e numbe r o f site s inth e estimatio no f nationa l ai r qualit y trend s outweigh s th e
8314 6 9 

## 72c

n

o o OOOOOOOOOXfc-~J0B00OW*kUlO»0>-J-»J-- J OOOO--«-'-'0(lWOI(JI0H0OKJNWWWW W -'-»w-J-*j)wvjioi- > ^o<ovoa)j>oa-*ooioiDi o

— » — » v j 0 0 - ' J > O l - J —'OOlDCfO i I O WOlJ J i j %- •—»— » OOOOOOOOOOOOOOOOOOOOO O OOOOOOOOOOOOOOOOOOOOO O
OOOOOOOOOOOOOOOOOOOOO O XkXkJ^«kJk.».^^^^XkJk.«.^^jNXi>.^tk.4kU1U I AJjJ»UlOlA*J>.UIK)-'N)*kUiVJIvlCTivlvlO\vJM( 0 OiOiWMfxWOOKJiOaXBMNJKOOMOW-'OOO M OOOOOOOOOOOOOOOOOOOOO O OOOOOOOOOOOOOOOOOOOOO O

OOOOOOOOOOOOOOOOOOOOO O obooooooooobooooooooo o ooooooooooooooooooooo o Jk^wJkVJirs)-«-'-*0OO0OOO00-*0-'K)U 1 is)W*Jio^ui~j~J*.^uiCTi~Ja»oo-~Jvo— ' i o A- • u i IOOIOWNIIOMIOCO-JWMJ^UIOUI-'UHOWUI W - a o

o

- 5

- a o o

o 3

- o

— 1 ro r + ro 3 ro V) VI • o

- h r + 3 " ro

-s ro J D c —, . - 5 ro a .

- a ro

- 5 O ro 3 c + C U I Q ro v

- a o p * o

- h a . c u r i - a l ro VI c + —jp

. = 1 c u r + ro Q . < C U

- 5 _ j

. C U 3 n ro tf < c u

-* ^- ^ XT ^-* *

- a o ^ ^ ^_ ^ * C U VI

- h c 3ctions = i C U X — 1

. = 1 c : =1 J D C C U

- 5 c + ro

- 5

_ ^ ^ <

— J ro > Q ) Q . tf > -e r —.»

- a o^_ ^

. . c u 3 a.
— J

. c+ I/ I — 1 1 > o o r — m c n i ^v l • m i/ > r + — J

. = ? c u r + ro a. o o >= 1

- o o i n — J

. r-t- ro c u < ro

-s c u 1X3 ro o

- hZ'A C D

C O j r

Q J -r j IAi— i ORE5-7uncticEstimated1msoftherequong-termleadireddatacomp

— ' O J ro -*•rqualityparateness,PQ. 3 roters IrL

Estimate d Mea n J>Requ

_. ro C L H 3 ? " ro

- 5 O ro 3 r-t-

r\ j . 0 0. 1 0. 2 0. 3 0. 1 0. 5 0. 6 0. 7 0. 8 0. 9 j i i i i ^ . ' i i 1 1 •#*
- +

+

+ -H
- +
+ + + -H - + + +

Estimate d Varianc e J

. 00 0 0.00 2 0.00 1 0.00 6 0.00 6 ^ p i

.. . i iRequii10.iredPei O ro 3 r + -l-H -+ i

T -r +

+

+ -H -
+
+ + + -H - + + +

SI - 1 1. 0 1

• c >

" O o "- »versu: IA

• o o

0.01 0 1(B)Var(1

• c >

- o o < ro i/ i c Vt o •

•

•

•

•

•

•

•

•

•

•P. 3



### TABL E5-8
. Estimate dcomposit eaverag eof 24-hou r TSP, u(p Q 

### ) , an

dit s estimate d variance , Var(p(p n 

### )) , a s function s of th e require dpercentage , p Q 

### , o f dat acompleteness
. p 

# o 30.6850 0 16.7120 0 16.7120 0 16.4380 0 16.4380 0 16.1640 0 15.8900 0 15.8900 0 15.6160 0 15.3420 0 15.0680 0 14.7950 0 14.2470 0 13.9730 0 12.8770 0 12.0550 0 10.4110 0 8.2190 0 6.0270 0 3.8360 0 0.2740 0 0.2740 0 A

u(P 0 ) 51.0819 9 53.5920 0 56.0500 0 56.5280 0 56.7260 0 56.5770 0 57.1089 9 57.1700 0 57.2869 9 57.4829 9 57.4350 0 57.2720 0 57.1340 0 57.2899 9 57.3100 0 57.4110 0 57.2049 9 56.9719 9 56.7820 0 56.7090 0 56.5259 9 56.5259 9 A" .



### Var( y 

### (p 0 

### ) ) 3.1793 1 1.3566 6 0.9099 8 0.6959 1 0.5204 5 9.2207 5 2.1236 0 1.1366 3 0.7509 1 0.5688 9 0.4440 4 4.1873 1 1.4216 3 0.8676 8 0.6587 2 0.5648 3 0.4551 0 3.0455 3 1 .3726 8 0.8746 3 0.6633 4 0.6633 4

8311+ 6 

### 7 5C

O U JFIGURasfuE5-8.Estimnotionsofthatedlong-terequiredermTSPadatacomp

ro - squatene i/> — i i/ i -p . » •r + < < -a o - a OJ

- 5 O lmeters J>10.Requi20.redPercent30.1 10

. p

S3

30. 0 1. 0 2. 0

K - TOIII20.30.10.equiredPercent +

+ +
+ + + ++ + + + 9L

Estimate d Mean 20

. 30

. 10

. i i <

Estimate d Varianc e 3. 0 1. 0 5. 0 6. 0 7. 0 p i i i i

+

+

+ 5 0

. 60

. p , i

- r + + 

# i i

< ro VI c l/ l •a o +

8. 0 9. 0 10. 0 1 1 1A(a)

c v 1 + ^ > O

< ro l/ l C 1/ 1 " O 

### o •

•

•

•

•

•

•

•

•

•

•

VdI O Q

_

• • u- o <L >

r

_ _ f O 3 C C c r a 

### £

- a C DEstimat

• O l 1 L O U JTABL i/ i 4->

- ^ c i a

• i c a . < r< # . a i o cexceeda

Q . 0 0hi- 3 O24-hctions

c 3 4

_ i/ l r o # . s - ^ CL. C r< S - < o (>riance,

( O >

- o C D 4-> r o E

co 0 )

• r o +- > < a 4 - o # » o C L

a i O l < a 4- > c a i t_ >redper

•r 3 C T C U i . O J

. c +-> 4 - O t o l/ l a > c a > +- > O J r —comp ^ ^

o CX

< r < S 0 3 < >
^—. . O Q

_ < r<

O Q .

• 0 0 r m I A i r o

o o O l CM i r C M

O O VO O l CM C M CM O

O O i r C M v O C M o C M *— o

o o o m m r - o o » — o

o o o CM CM O vO l CM

O O o o o o , — o o o r~ o o

o o 0 0 r r ^

o o m CM CM 0 0 CO C O v O o r o v — r v O ^ ~ r- » v O r o i r V O r o ^- V O " » o *3 - i r C M » —

o o o o V O

o o i r v o » — V O

• * T O i n i r r o O

o o o o m r -

o o o O l o o i n r o v o i n 0 0 • — o

o o CM r~ v o

o o o at o o m r o o v O ^ ~ « - • o

o o o o r o m

o o v o » — v O m r o T r o o o o O

o o O l o » i r

o o CM i r r o m m r o r o v o O O

o o i n r o i r

o o o o VO o m r o v o r o r m o

o o v O r r o

o o i n O l r - "«r

• m o O l 0 0 * — o

o o m < r » t o

O o r ~ i r CM i r r r o « *

. — .— * o

o o 0 0 CM r o

o o r o r ^ O t 0 0 VO O l

. — 9— o

o o VO i r i r

o o r ^ r-» o o r ~ 0 0 O l o o CM C M o ^ - • — o o

o o 1 — O l m

o o i n i n o o o ^ ~ r ~ m

o o

. —

. — " » r o C M C M o » •

T — m v o 0 0 0 0 V * o » V O C M o

o o O l i r m

o o O l v — r o i r o a o » — o o

o o m r o m

o o r - CM o o « — ao i r

o o v O r o r o T ao l O »~ » o

o o r - o o i r

o o i r r ^ C M O OO C M 0 0

• v o r o o VO ^ — O O r o 9— o

o o r - o o ^-

o o i r r ^ CM O •

• • • r- ^ r-« .

j
- .— 4 CO 0 0

•2L

O J IA

- h C 3 r > c+ _ j

. o 3 t o O

- h r + ZT ro

- s ro J D C ^ .
- ? ro C L C L O Jtrial o o 3

- a

— J ro r + ro 3 ro I O t o «

- o o

. - n t— ) (7 1 < = P O m a i i v o

. m t o rt - —t. 3 O J rt ro Q

. t O 3 " o

- 5 rt rt ro

- s 3

- H C/ l

- a O J — j

.

- i J D e Q J

— J . J

. r i
- < <

- a O J

- 5 O J 3 ro ro

- 5 IA 

### Estimate dExceedanc e Rat e

J3. 0 0. 5 1. 0 1. 5 2. 0 2. 5
" " l 1 1 .J . I I 3. 0

TO ro J D c ro o .

- o ro

- s o ro 3 + + + +
T ++ 4 ^++ t ^ \ + - a o < ro

- i l/ l c t o T 3 O

s M Estimate d Varianc e

0, 5 1, 0 1, 5 2. 0

73 ro J D c ro C L -o ro

- s o ro 3 PS

) + + + + 

## XA C O

O l

- J

• o O < ro

- j i/i c l/ l

- o o

9' dP. 7



### disadvantage s o f includin gsite s havin ga lo w data completenes s propor tion , p.: . The broa dinferenc e fro m thes e 198 1 results—whic hseem s likel y t o hol deve ni f mor e frequen t intermitten t sample s o r mor e complet e dat a ar e required--i s tha t th e wid e variabilit y betwee nsite s argue s agains t th e exclusio no f a larg e fractio no f site s solel y o nth e basi s o f data completenes s requirements . Thus , th e requiremen t fo r estimatin gnationa l trend s shoul dbe mor e relaxe dtha ntha t fo r determinin gth e attainmen t statu s o f a

nindividua l site .

8314 6 9 7 9P. 8

REFERENCE S

Abramowitz , M. , and I . A . Stegun . 1964 . Handboo k o f Mathematica l Function s . Dover , Ne wYork . Brillinger , D . R . 1969 . Th e calculatio n o f cumulant s vi a conditioning
. Annal s Inst . Stat . Math. , 21:215-218 . Breiman , L. , J . Gins , and C . J . Stone . 1978 . "Statistica l Analysi s and Interpretatio n o f Pea k Ai r Pollutio n Measurements. " Technolog y Servic e Corporation , Sant a Monica , C A(TSC-PD-A190-10) . Breiman , L. , and C . J . Stone . 1983 . "Approximat e confidenc e bound s fo r extrem e quantiles. " In progres s (wil l b e submitte d i n 198 4 t o J . Amer . Stat . Assoc.) . Cochran , W . G . 1963 . Samplin g Techniques , 2nd ed . Wiley , Ne wYork . Davidson , J . E. , an d P . K . Hopke . 1983 . "Implication s o f Incomplet e Samplin g i n a Statistica l For mo f th e Ambien t Ai r Qualit yStandar d fo r Particulat e Matter. " Pape r presente d a t th e 76t hAnnua l Meetin g o f th e Ai r Pollutio n Contro l Association , Atlanta , Georgia
. EPA . 1979 . "Guidelin e fo r th e Interpretatio n o f Ozon e Ai r Qualit y Standards. " Environmenta l Protectio n Agency , Researc hTriangl e Park , NC (EPA-450/4-79-003) . EPA . 1981 . "U.S . Environmenta l Protectio n Agenc yIntra-Agenc yTas k Forc e Repor t o n Ai r Qualit yIndicators. " Environmenta l Protectio n Agency , Researc hTriangl e Park , NC (EPA-450/4-81-015) . Frank , N . H. , and T . C . Curran . 1982 . "Statistica l Aspect s o f a 24-Hou r Nationa l Ambien t Ai r Qualit yStandar d fo r Particulat e Matter. " Pape r presente d a t th e 75t hAnnua l Meetin g o f th e Ai r Pollutio n Contro l Association , Ne wOrleans , Louisiana , Jun e 1982 .

8314 6 6 3 0P. 9



### Hajek , J . 1960 . Limitin gdistribution s i nsimpl e random samplin gfrom

a finit e population . Pub . Math . Inst . Hungaria nAcad . Sci. , 5:361-374 . Johnson , N . L. , andS . Kotz . 1969 . Discret e Distributions , Wiley , New York .

8314 6 6 

### o lP.1 0



### Appendi xA SULFU RDIOXID E DESCRIPTIVE STATISTIC S

8314 6 5P.1 1

APPENDI XA SULFUR DIOXID E DESCRIPTIV E STATISTIC S

Usin g progra mNA28 2 o f th e EPA' s Storag e and Retrieva l o f Aerometri c Dat a (SAROAD ) system , variou s summar yfile s o f 198 1 dat a wer e obtained
. Figur e A- l show s th e distributio n o f dat a completenes s amon g ai r monitorin g stations
. Tabl e A- l show s th e numbe r o f site s meetin g an annua l quot a o f 7 5 percen t dat a completenes s and meetin g a correspondin g quarterl yquot a i n a specifie d numbe r o f quarters . Tabl e A- 2 show s th e sam e informatio n fo r an annua l quot a o f 5 0 percent . I n considerin g whethe r a quarterl y quot a shoul d b e require d s o tha t an eve n distributio n o f dat a woul d b e guarantee d throughou t th e season s o f th e year , i t i s o f interes t t o not e tha t roughl y7 0 percen t o f th e site s tha t me t an annua l quot a als o me t a correspondin g quarterl yquot a i n eac ho f th e fou r quarter s o f 1981 .
Ther e ar e lowcorrelation s betwee n th e percentag e o f dat a completenes s and reporte d annua l averages , standar d deviations , and coefficient s o f variatio n (Figur e A-2) . Histogram s o f "expected " exceedance s (i.e. , X. * o r k./p. i n th e notatio n o f Sectio n 2 ) sho wtha t zer o expecte d exceedance s wer e reporte d b y9 6 percen t o f th e station s (Figur e A-3) . Th e greates t numbe r o f expecte d exceedance s wer e reporte d b

yth e station s tha t ha d th e mos t complet e dat a (Figur e A- 4 and Tabl e A-3) .

8314 6 5SY1S30 LCOUN T A4 I

2 EACI I SYMBO LREPRESENT S MEA N ST.DEV . 70.98 7 23.05 8 1 OBSERVATIO N

' •3

. . * 6

. . * ' J

. . * 1 •! * 1 5 ••Mb * 2 i * 2 4
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• - + - 1. 7 2. 7 - - + - 4. 7 - - + - 4 5 - - + -

6 0 - - + - 0 5 — + - 7 0 — + - 7 b - - + 8 0 FREQUENC Y INT . CUM . 3 0 2 3 0 7 2 Q 5
G 6 7 6 3 3 7 4 9 9 8 3 1 5 1 2 1 6 2 1 2 6 4 0 5 1 6 0 7 5 3 8 1 0 0 0 0 3 3 5 8 8 1 5 1 7 2 5 3 0 3 5 4 0 4 6 5 2 5 9 6 5 6 8 7 1 7 8 8 2 9 1 10 0 10 8 11 1 12 6 13 8 15 4 17 5 20 1 24 1 2 9 2 35 8 4 3 3 47 1 47 2 47 2 4 7 2 4 7 2 47 2 PERCENTAG E INT . CUM . 0. 6 0. 0 0. 4 0. 6 0. 0 1 . 5 0. 4 1 . 7 1 . 1 1 . 1 1 . 1 1 . 3 1 . 1 . 1 . 0 . 0 . 1 . 3 5 3 6 6 5 0. 8 1 . 9

, 9

, 7 6 2 5

, 4 4 5

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8314 6 FIGUR E A-la

. Histogra m o fth e percentage , p , reportin g one-hou rS0 9 concentration s i n 198 1 o fdat a completenes s fo rthos e site s : SLAM S (includin g NAMS)

.P.1 3

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### TABL E A-l . Numbe r o f site s recordin g one-hou r S0 £ durin g a t leas t 7 5 percen t o f th e tota l possibl e hour s i n 1981 , b yth e numbe r o f quarter s i n whic ha t leas t 7 5 percen t o f th e tota l hour s pe r quarte r ar e recorded .

Sit e Typ e
SLAM S (include s

Othe r No . o f Quarter s

wit ha t Leas t 75 % Hours/Quarte r 

### 0 NAMS ) 1



### 2 3 4 Tota l 

### 0 1 2 3 4 Tota l No . o f

> 75 % 

### 0 0 1 7 7 9 

### 24 4 34 0 

### 0 0 8 4 8 

### 11 7 17 3 Sit

< 

### e s 

### 75 % 3 5 4 8 3 8 

### 9 0 13 0 4 9 8 9 5 1 5 9 

### 0 24 8 Tota l



### 3 5 4 8 5 5 8 8 

### 24 4 47 0 4 9 8 9 5 9 

### 10 7 11 7 42 1

TABL E A-2 . Numbe r o f site s recordin g one-hou r S0 2 

### durin g a t leas t 5 0 percen t o f th e tota l possibl e hour s i n 1981 , b yth e numbe r o f quarter s i n whic ha t leas t 5 0 percen t o f th e tota l possibl e hour s pe r quarte r ar e recorded .

Sit e Typ e
SLAM S (include s

Othe r 

### No .

w i 50 %

NAMS ) 

### o f Quarter s t ha t Leas t Hours/Quarte r 

### 0 1 2 3 4 Tota l 

### 0 1 2 3 4 Tota l No . o f

> 50 % 

### 0 0 1 9 4 2 

### 34 3 ~40 4 

### 0 1 1 5 

### 10 2 17 0 "28 8 Site s

< 50 % 

### 11 3 0 2 5 

### 0 0 66 f 2 4 4 8 5 9 

### 2 0 "l33 ~ Tota l



### 11 3 0 4 4 4 2 

### 34 3 "470 " 

### 2 4 4 9 7 4 

### 10 4 17 0 

### "42 1

8314 6 8P.1 5

SLAM S OTHE R N = 89 1 CO R= .050 0

SYMBOL ' SYMBOL '

MEA N 70.- J 9 3 25.02 6 ST.DEV .

25.78 2 16.53 9 REGRESSIO N

X = ..0SG73* Y+ Y = .03313*X + LIN E

68.32 8 22.47 9 R E S.MS .

603.3 6 276.3 0

c o

O O o o o t o 140. 0 +

122. 5 +

105. 0 +

!7.5 0 +

70.0 0 +

5, a

35.0. 7 + S O

. 0 0 x+ . , + *

0 S
0 0 1 7 . 5. 7 S O 0 O S SO S s s s s s 0 s s 0 * 0 O S s 0 0 0 0 s ssooo o 0 0 s s SS S O S O S 0 s s soo o 0

s I 0 0 s O S

0 s
* s s o s s s s s s

0 00 0 i: 0 S O 0 S 0 0 0 OO S 00 0 SO O *0 0 S 0 0 S S OOOSuOOOCO O 0 O S 00 0 s * 0 o*ss* o 0 s 00*0000* S 0 SSSSS00**00 S s * *oo o *so*s * SS * S0-0"* 0 s * 0 OOOO * 0 sssoooo****so *

O S 000*0 0 S O S OS O 0* S 0 S O S o OS

00 * OS SOO OO S SO

0 0 0 OS"O O SS S 00 * 0 0 0 0 * 0

0 O S 0 0* 0 0"S '

0 0 0 S 0 s s s s s s S O S s s O S SO S 0 ssss o OS S * * 0 s s s s • ssssosss s s s s*sso s s s*s*ss*** o s s*ss•=*** * *os***s***o *
* ft 2 * * 2 * ft -.

V ft ft ft •. 1 ft S ***** * s * s 00******0** 0 ft ft Q * * * * ft ft g ft 2 ss**sos*s o ooss * *••• •••SS S o*s* s *S*SS S 0.00 0 + 7.50 0 22.5 0 37.5 0 52.5 0 67.5 0 8 2 r.00 0 15.0 0 30.0 0 45.0 0 60.0 0 75.0 0 h ... . + ... . + .. . . .5 0 9 7.5 0 90.0 0 105. 0

FIGUR E A-2a . Scatte r plot s o f ambien t ai r qualit y statistic s versu s th e percen t o f tota l hour s recorde di n198 1 (p ) b y site s reportin g one-hou r S0 2 : annua l averag e versu s p .

831'* 6P.1 6

SL/VM S OTHE R N » 89 1 CUR = .071 2 SYMBOL- S

SYMBOL= 0

liEA N ST.DEV

. REGRESSIO N LI.M " RES

. MS

. X7.V.49 3 25.73 2 X = . ,: C C J 3 - Y+ G'J.iS a 632.0 7 Y39.20 5 32.73 8 Y = ..0iz.J3o V; X+ 32.^ 5 10G7. 6

A0 0

3 5 0 +

3 0 0

ir-. 7

5 0

.

0 . 0 , + .X

. . + *

s 0

0 0

s s 0 0 0 0 ' O S

0
' 0 S 0 0 0 * S S s o OS OS

. + . . S 0 0 OS

* 0 0 0 0 OS O 0 00 3 S Sz'O-' O *•• * S SO 0 Ou'- * •••O S O O — S S 'J'J'- S SC O SO '•60-' :/ 00t 0 SO 00S00S ; ' o
- 00 OS S 0 0 s s 0 0 i t 0 0 0 s 0 0 0 0 oo o s o-os s 3 0 * OSOOO O 0 •• • 0 0 S S S SOOOO—'O S '••0 0 OO S SSOSSO O * : -0-'O v
• •0 S 0 0 • •00 3 * S * S * ••

' * - •'• •S0 3 S 'S S "0* S' ; 3 S 0 0 T 3-'-3S 3 0 0 * *0S 0 0 0 OS O 0 0 0 0 S + ... . + ... . + .. . .X.... + s 0 0 * s O S * 3 1.13*0 * s**~ s **s* * ft* 0 - - 0 0 0

. . . + . s s

O O S 0 0 0 OS OS 0 *SSS S S* S — * 3**** * p f* * ft ft ft

.- . ft ft s * ft 3-*S*- : S S s s

0 s 5 0 ft **o o ftQft* s * o p V rt rt rt ••s * s •• * s * s ••SS S OSS S + . , 7.50. 7 22.5. 7 37.5. 7 52.5 0 67.5 0 02.5 0 97.5 0 .7.7 0 1 5 . .70 30.0 0 45.0 0 60.0 0 7 5 .0 0 9 0 .0 0 105. 0



### FIGUR E A-2b . Standar ddeviatio nversu s p .

8314 6P.1 7

SLAM S OTHE R M - 89 1 COR=-.033 0 MEA N X70.-19 3 Y1.634 5 SYMBOL- S

SYMBOL- 0

ST.DEV . REGRESSIO NLIN E RES.M S 25.73 2 X= -1.0_'73*Y + 72.17 3 664.7 2 .8274 9 Y--..O0106*X + 1.707 1 .684/ 7

C 0 t i 12. 0 +

.7. 5

7 . 5. 7

3.0 0

1 . 3 0

0.0 0 +....+....+....+ . ,X+ .

O O 0 

## s o

O S
OOO O O S O OO O *OSO O s O S 

### s o 0 0 0 O S O S O- O 0 * 0 0 00 0 O S 0 0 * 0 0 0 0 0 0 0

0 0 00 0 0 0 S O 0 0 0 SO O S 0

0 0 0*0 0

00 0 0*0*0" * O S



### s

s o SO O • *0- 0 s *-'-0-5S ; SO S 0 O S 0 0 3 0 0 •S'-30*" f s "3'0- : : : O S 0" " 0 S l 0 

### •s
ft

OS O 0*'0 0 OOS S 0**3**0"" S

0 0 500-0" * 0 0 0 000 3 *000"000 s

• i .'5 S0'- : * * S SSS-S"Z:0"S*S * • 3 SSS*
- S O 0 S 3 S S 0 s 0 0

. . . + + ... . + ..X

. + . O J * ' s * s s s s s 0 0 0 0 0 s Q rt-. va * s * * ft * * o * ft 0 ft s ft* s**s* * S**S3 * SS ' s * 0 0 0 * s 0 0 -* o *os* o s S* 0 "* 0 ftft:' : '.•. •* ft ft ftft ft ft * ft rt "*-"*S S SS**SS S 0 0 0

7.50 0 22.5. 7 37.3. 7 52.5 0 67.5 0 82.5 0 97.5 0 0.00 0 15.0 0 30.0 0 4 5.0 0 60.0 0 7 5.0 0 90.0 0 105. 0



### FIGUR E A-2c . Coefficien t o f variatio n(standar ddeviatio ndivide db y annua l average ) versu s p .

8314 6

### E

* . 6000.0 0 * 1.2000 0 * 1 . 0000 0 * 2 . 100,0. 7 *3.000 r '' 0 •3.000.0 0 * 4 . 200.0 0 * 4 . 8 00.0 0 * 5 . 4.000. 0 * 6.00.0.0 0 * 6.6000 0 * > .20.0,0 0 * 7.0000 0 *3. 4 0000 . -'J . '"?ri7 0 * 7.6000 0 -10.2.00 0 -10.000, 7 * J 1 . '1.00 0 * 1 2 .070. 7

- l 2 . a n 'T

O * 1 3 . 20.0 0 -13 . 000. 7 -14.4.0 : 0 rt ] 5 ,0.00 0 ••15 . 6'.:-.. ' ••'••1 6 . 2 r '"' ( « 16.80.0 0 * 17 . 4 >•'"• / * 1 8 . .O0/.. 0 * 18 . 6.'00. 7 * 1 9 . 2.0.0. 7 * 19.0°" J "20.400 0 -21.0.0.0 0 *2 1 .0.0.0: 7

- 2 2.2.00 0

- 2 2.800 0 •AA A

• AA A +AA A +A A + A + + + + + + + + A + A + A A + + + + + + + + + + + + + + A + + + A + + t
- + + + 5 '.AAA ; ^

'

5 1 0 .AAA ,

1 0 SYMBO L COUN T MEA N ST.DEV . A4 7 0 0.18 0 1.42 4 EAC H SYMBO L REPRESENT S 1 OBSERVATIO N 1 5 2 0 2 5 3 0 3 5 4 0 4 5 5 0 5 5 6 0 6 5 7 0 7 5 8 0 \AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA *

1 5 2 0 2 5 3 0 3 5 4 0 4 5 5 0 5 5 6 0 6 5 7 0 7 5 8 0 FREQUENC Y INT . 45 4 4 3 2 1 0 0 0 0 0 0

- 0 1 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 CUM . 45 4 45 8 4C 1 46 3 46 4 46 4 45 4 4 6 4 46 4 46 4 46 4 4 6 4 4 0 5 4G G 4 6 8 4 0 8 4 0 8 4 0 o 4 0 8 4 6 8 4 0 8 4 0 {i 4 5 0 4 G8 40? ! 4 0 8 4 '.'
- 8 4 C 8 4 0 * J 4 0 7 4 C i 7 4/ 0 4/ 0 4 7 0 4/ 0 4 7 0 4 7 0 4 7 0 PERCENTAG E INT . 96. 6 0. 9 0. 6 0. 4 0. 2 0. 0 0.0 0.0 0.0 0.0 0.0 0.0 0. 2 0. 2 0. 4 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 w . 1,1 0. 0 0. 0 0. 2 0. 0 0. 0 0. 2 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 CUM . 96. 6 97. 4 98 . 1 98. 5 98. 7 98. 7 98. 7 98. 7 98. 7 98. 7 98. 7 98. 7 93. 9 9 9 . 1 99. 6 99. 6 9 9. 6 9 9. 6 9 9. 6 99. 6 9 7. 6 9 9. 6 9 9. 6 97. 6 97. 6 9 7 . 6 9 7. 6 9 7. 6 9 7 . 8 9 9 . 8 9 9 . 8 10.7. 0 1 0 0 . 0 1 00. 0 100. 0 1.0.7. 0 100. 0 1 0 0 . 0
Numbe r o f Site :

FIGURE A-3a . Histogra mo f expecte d primar yexceedances , E , fo r thos e site s reporting 24-hou r S0 2 concentration s i n 1981 : SLAM S (includin g NAMS) . 8314 6

## E

* . G0.700. 0

- 1 . 20.00 0
* i , o.i;.i.!.o. o * 2 . 4.000.0 0 * 3 . 0 0 0 O' O * 3 . 60.90 0 * 4 . 2 000: 7 * 4.8 7 0 0 0 * 5 . 40.00 0 * 6.0000 0 •"6.0.7. 0 0 0 * 7 . 200:7 0 '•7.0000 0 * 0.4000 0 V; 9 .00.00. 7 * 7 . 60,00 0 * 1 0 . 20.0 0 * irr.U' 10 7 *1 1 . '10.0 0 * 12.0.0.0 0 * 1 2 . 0.0.0 0 ft i 3 , 2.7.0. 7 * 1. 3 . 8.0.0 0 * 14 . 40.0 0 * 1 5 .0.0.0. 7 * 15 . o.o.o.'" / * 16 . 2.0.0 0 * 1 6 . 8.00. 7 * 17 . 4.0.0 0 •18.000 0 -18 . 6,0.0. 7 * 19.200 0 * 19.800 0 * 20.400 0 * 2 1 .000. 7 *21.OOO O * 2 2 . 2 0.0 0 * 22.800 0 5

+13I J 0 B C B B ' +BBBBBEB 1 + 0 u + 8 + I3 B + B + B + + + + + + + + + + + + + + + + + •i

- + + + + + + + + ' + + + + + 5 S '

EAC H SYMBO L 1 0 1 5 2 0 2 5

• G3 B 1 3 C Gl i 1 3 £ ' 3 131 3 D 5 u ' J •_

> L
• 131 3

1 0 1 5 2 0 2 5 'M30 L COUN T MEA N ST.DEV . B 42 1 0.06 7 0.38 0 REPRESENT S 1 OBSERVATIO N 3. 7 3 5 4 0 4 5 5 0 5 5 6 0 6 5 7 0 7 5 8 0 .'uUoOBBIj'.-BUBBUBBBOCUoBC-BBBBOBBBBUBBBBBBBBBBBBBBBBBBBBBB *

3 0 3 5 4 0 4 5 5 0 5 5 6 0 6 5 7 0 7 6 8 0 FREQUENC Y INT . 40 4 1 0 2 1 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 CUM . 40 4 41 4 41 6 41 7 41 9 4 2 0 42 1 42 1 42 1 42 1 42 1 42 1 42 1 42 1 42 1 4 2 1 42 1 42 1 42 1 42 1 42 1 42 1 42 1 42 1 42 1 42 1 42 1 4 2 1 42 1 42 1 42 1 42 1 42 1 42 1 42 1 42 1 42 1 42 1 PERCENTAG E INT . 96. 0 2. 4 0. 5 0. 2 0. 5 0. 2 0. 2 0. 0 0. 0 0. 0 0. 0 0.0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 CUM . 96. 0 98. 3 9 3. 8 9 7. 0 99. 5 99. 8 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 10 0 . 0 100. 0 100. 0 10 0 . 0 1 0 0 . 0 10 0 . 0 10.7. 0 1 0 0 . 0 100 . ti 100. 0 10.7. 0 1 07. 0 10.7. 0 100. 0 10.7. 0 107. 0 1 0.7. 0 1 XI . kl 1 0 0 . 0

FIGUR E A-3b

. Othe rsite s 8314 6P.2 0

SLAM S , SYMBOL= S OTHE R , SYMBOL= 0 N = 89 1 COR = .081 3 MEA NST.DEV . REGRESSIO NLIN Z RES.MS . X 70.45 7 26.0-3 3 X = 1.S335-Y + 70.20 5 675.5 5 Y .1268 2 1.067 9 Y = .00333*X-.107S 0 1.134 1

20. 0 +

17. 5 +

15. 0 +

lZ. 5 +

E 10. 0

7 . 5. 0 +

5 . .7. 7 +

2 . 5. 7 + , + *

0 0
SOO S 0 

### s

s

0 0 s s 0*00 * 0* * 0. 0 0 y

* o * 0 * * * * 0 0 * * 0 **************** * 0 **** * 0 ********************'•***"*" * * * S Y +....+....+....+....+....+....+....+....+....+X . . . + ... . + ... . + . . , . + .... + . 7.50 0 22.5 0 37.5 0 52.5 0 67.5 0 82.5 0 97.5 0 0.7 0 15.0 0 30.0 0 45.0 0 60.0 0 75.0 0 90.0 0 105. 0

FIGUR E A-4 . Scatte r plo t o f th e percentag e o f tota l day s recorde di n198 1 (p ) versu s expecte dprimar y exceedance s (E ) fo r site s reportin g24-hou r averag e SOp .

831«* 6TABL E A-3 . Frequencytabl e o f the percentag e o f tota l day s recorde d in 198 1 (p )

versu s expecte d primar yexceedance s (E ) fo r site s reporting 24-hou r SC L averages ,

0 - 28 X < = 43 X <« = 60.5 X 71X 80 X < - 86 X< - 89.5 X< - 93X< - 96.5 X< - 100 X TOTA L 0 1 < - 2 < - 5 < » 2 0 TOTA L 8 7 pf f 0 1 0 8 8 8 8



### e r 0 0 0 8 8 8 7

0 0 0 0 8 7 8 8

0 1 2 0 9 1 8 8

0 5 1 0 9 4 8 1

0 1 1 0 8 3 7 7

0 3 0 0 8 0 8 7

2 1 0 3 9 3 10 2

2 0 2 2 10 8 7 3

4 1 0 1 7 9 85 8



### e 1 2 7 6 89 1

8314 6P.2 2



### Appendi xB CARBON MONOXID E DESCRIPTIVE STATISTIC S

8314 6 5P.2 3

APPENDI XB CARBON MONOXID E DESCRIPTIV E STATISTIC S

Figur e B- l show s th e distributio no f dat a completenes s amon gai r monitor in gstations . Tabl e B- l show s th e numbe r o f site s meetin ga nannua l quot a o f 7 5 percen t dat a completenes s an dmeetin ga correspondin gquarterl y quot a ina specifie dnumbe r o f quarters . Tabl e B- 2 show s th e same informatio nfo r a

nannua l quot a o f 5 0 percent . Not e tha t roughl y 8 0 percen t o f th e site s tha t me t a nannua l quot a als o me t a correspondin g quarterl y

quot a ineac ho f th e fou r quarter s o f 1981 . Histogram s o f one-hou r expecte dexceedance s show tha t zer o expecte d exceedance s wer e reporte db y 9 9 percen t o f th e station s (Figur e B-2) . A scatte r diagram

o f expecte done-hou r exceedanc e versu s percentag e o f dat a completenes s i s presente dinFigur e B- 3 andinTabl e B-3 . Simila r informatio nfo r overlappin geight-hou r exceedance s i s show nin Figure s B- 4 an

dB- 5 an

dinTabl e B-4 . Mor e tha n1 0 percen t o f th e station s reporte dove r 4 0 expecte dexceedances . N o clea r relationshi p betwee nexpecte dexceedance s an ddat a completenes s i s evident .

8314 6 5SYMBO L COUN T A36 4 EAC H SYMBO L REPRESENT S MEA NST.DEV . 80.35 3 23.13 8 1 OBSERVATIO N P 5 1 0 1 5 202 5 3. 7 3 5 4 0 4 5 5 0 5 5 6 0 6 5 7 0

+ v

+ + + + + + + + + + + + + . * 3..7000 0 + A * 6..00. : 070 + A * 7.000,7

. J + A * 1 2 .000. J +AA A *15.00:.-. z +A A * 1 8 .07.'.'

. J + * 2 l A)X.!.i

+ A * 2-i.H :.<•<.! + AAAAA A *2V .0

. •-. ; +AAAAAAA A *-l/

. 0..O. 7 +AA A * 3 '.3.0.77. 7 +A A * 3 0 .:).'i.0', 7 +AAA A * 3 7.0.'. ' 0. 0 +AA A * 4 2 .0,00. 7 + AAAAAA A * 4 5.00.0 0 + A * 4 o . .1)00. 7 + AAAAA A * 6 1 .0.0:0 7 +A A
' * 5 4 .0.'.':.'.'.' ' + * 5 7.0.0:.'

. J +AA A *-30.0.1.'0. 7 +AA A * 6 3.0.0.0 0 +AA A".AA A *uu.0.0.7 0 + AAAAAAA A *G 7 .0.Z-.'...; i + AAA/-.AAAA A *'/2.0.7'.-J J *

AAAAA A * 7 5 .0:07 0 +AAAAAAAAAA A * 78.00.0 0 +AAAAAA A * 3 1 .000. 7 +AAAAA A *8-- ! .:):".•.!

+A.'•..".AAA." .AAAAAAAA * 8 /

. .000. 7 + A.' \

AA AAAA AAA A * yJ J ..';.-.'0: 7 + AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA A * : 7 3 .0 7 0 '. 7 + AA AA.V. AAAA AAAA AAA AAAA AAAAA AAAA AAAAAAA AAAAAAAAA A * 70.07.0 0 + AAAAAA

. .AAA.V.AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA A *9' J ..O.oi' 0 +A."..'z.\AA.^..'zAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA A * 10 2

. .0.0. 7 +AAAAAAAAAAAAAA A *l.l:'i..0.0.t f+ * 1 JL < 8
. .7.0 0 + * 1 1 1
. 00. 7 + * 114.00 0 +

+ + + + + + + + + + + - 5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5 5 0 7 5

- - + - 8 0

- - +

- - + - i;i ; 6 0 -- + -

6 5 - + - 7 0 7 5 8 0 FREQUENC Y

INT . CUM . 1 1 1 3 ' 2 0 1 6 8 3 2 4 3 7 1 6 2 0 3 3 7 8 9 6 1 1 7 6 1 5 1 2 3 3 4 7 6 1 7 0 1 5 0 0 0 0 1 2 3 6 8 8 9 1 5 2 3 2 6 2 8 3 2 3 5 4 2 4 3 4 9 5 1 5 1 5 4 5 7 6 4 7 2 8 1 8 7 9 8 10 5 U l 12 6 13 8 17 1 21 8 27 9 34 9 36 4 36 4 36 4 36 4 36 4 PERCENTAG E INT . CUM . 0. 3 0. 3 0. 3 0. 8 0. 5 0. 0 0. 3 1 . 6 2. 2 0. 8
0. 8 1 . 9 0 . 1 . 0 . 0. 0 0. 8 0. 8 1 . 9 2 , 2 , 1 . 3 , 1 . 1 , 4 , 3 , 9 . 12 , 16. 8 19. 2 4 . 1 0. 0 0. 0 0. 0 0. 0 0. 3 0. 5 0. 8

8. 8 9. 6 1 1 . 5 1 1 . 8 13. 5 14. 0 14. 0 14. 8 15. 7 17. 6 19. 8 22. 3 23. 9 26. 9 28. 8 30. 5 34. 6 37. 9 47. 0 59. 9 76. 6 95. 9 100. 0 100. 0 100. 0 100. 0 100. 0
Numbe ro fSite s

8314 6 FIGUR E B-la

. Histogra m o fth e percentage , p , o fdat a completenes s fo rthos e site s reportin g C O i n 1981 : SLAM

S (includin g NAMS)

.

### p *3.0000 0 * 6..0.700 0 * 9.07.70. 7 * 1 2 .000 0 *• ' 1 !i
. 0.7.7. 7 * 18.000 0 * 21.000 0 *2- J Q r<>'t * 2 7 . 000: 7 * 3 0.0.O.. ) . 7 *33..O.j0 0 *36.0.7.J 0 * 3 9 . 0.0.7 0 * 4 2.0.O.7 J
" 4 5.0.7:,'. 7 * 4 8 .00.7.0 ' •51.000. 7 * 5 4 .0.7.7. 7 •57.00.7 0 * 6 0.00.0. 7 * 6 3.000. 7 •66.007. 7 •69.000 0 •72 . 0. 0 0 0 *75.0n-' 7 * 7 8.0. 0 0. 7 • 3 1 .0.0.7. 7 •34.007 0
• 3 7.00.0. 7 •90.0.0.0. 7 * 9 3.0.0.0. 7 * 9 6.0. 0 0 0 •97.000. 7 * 102 . fi,70 * 1.7 5 .077 0 *1IZP3..O:7 0 *l11.00 0 * 1 1 4 . .0.7 0 5

+ B B + 8H B + 0 3 + B + L ; 8 +;: c +Li:..r, B + .V:.!.A,;3 3 + U0B B + 8i; C + !!'.'i.:!!B' 3 + i;u.B U + R01A.. B •t-UI.Li B + 8 8 + + B + + B + + H3 B + D + !A.'L; 3 + 8iiBB B + B B + +I;L.[, B + BL.L I + 8 1 0

+ G:v 1 ;833BBBBB B + 8 0 6B' . +GLBBB L •*

- L J C L 3 B t > + + + + + 5 EAC i 1 5

BBBBBBB B 3 B B

1 0 1 5 1 S Y

2 0

2 0 SYMBO L •IBO L 2 5

2 5 B COUN T

1 1 6 REPRESENT S 3 0

3 0 3 5

3 5 4 0

4 0 MEA N 58.95 2 1 4 5

4 5 ST.DE V

3 1 . , 63 1 OBSERVATIO N 5 0

5 0 5 5

5 5 6 0

6 0 6 5

6 5 7 0

7 0 7 5

7 5 8 0

8 0 FREQUENC Y INT . 2 3 2

, 1 2 2 4 6 4 3 6 4 5 4 2 0 1 0 1 0 3 1 4 5 2 0 4 3 1 1 2 1 4 7 8 0 0 0 0 0 CUM . 2 5 7 8 1 0 1 2 1 6 2 2 2 6 2 9 3 5 3 9 4 4 4 8 5 0 5 0 5 1 5 1 5 2 5 2 5 5 5 6 6 0 6 5 6 7 6 7 7 1 7 4 7 5 8 7 10 1 10 8 11 6 1 1 6 11 6 11 6 1 1 6 11 6 PERCENTAG E INT . 1. 7 2. 6 1 . 7 0. 9 1 . 7 1 . 7 3. 4 5. 2 3. 4 2. 6 5. 2 3. 4 4. 3 3. 4 1 . 7 0. 0 0. 9 0. 0 0. 9 0. 0 2. 6 0. 9 3. 4 4. 3 1 . 7 0. 0 3. 4 2. 6 0. 9 10. 3 12 . 1 6. 0 6. 9 0. 0 0. 0 0. 0 0. 0 0. 0 CUM .

1 . 7 4. 3 6. 0 6. 9 8. 6 10. 3 13. 8 19. 0 22. 4 25. 0 30. 2 33. 6 37. 9 4 1 . 4 43 . 1 43 . 1 44. 0 44. 0 44. 8 44. 8 47. 4 48. 3 5 1 . 7 56. 0 57. 8 57. 8 6 1 . 2 63. 8 64. 7 75. 0 87 . 1 93 . 1 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 Numbe ro fSite s
FIGURE B-lb

. Othe rsites , 8314 6P.2 6



### TABL E B-l . Numbe r o f site s recordin g C O durin g a t leas t 7 5 percen t o f th e tota l possibl e hour s i n1981 , b yth e numbe r o f quarter s i n whic ha t leas t 7 5 percen t o f th e tota l hour s pe r quarte r ar e recorded .

Sit e Typ e
SLAM S (include s

Othe r 

### N

o w i 

### 75 %

NAMS ) . o f Quarter s t ha t Leas t Hours/Quarte r 

### 0 1 2 3 4 Tota l 

### 0 1 2 3 4 Tota l N o . o f Site s

> 75 % 

### 0 0 5 5 3 

### 21 2 27 0 

### 0 0 0 1 7 3 3 5 0 < 75 %



### 15 3 7 3 3 

### 9 0 9 4 1 7 3 5 

### 8 6 0 6 6 Tota l



### 15 3 7 3 8 6 2 

### 21 2 36 4 17 3 5 

### 8 2 3 3 3 

### 11 6

TABL E B-2 . Numbe r o f site s recordin g C O durin g a t leas t 5 0 percen t o f th e tota l possibl e hour s i n1981 , b yth e numbe r o f quarter s i nwhic h a t leas t 5 0 percen t o f th e tota l hour s pe r quarte r ar e recorded .



### No , o f Quarter s wit ha t Leas t Sit e Typ e 50 %
SLAM S (include s NAMS )

Othe r Hours/Quarte r 

### 0 1 2 3 4 Tota l 

### 0 1 2 3 4 Tota l N o . o f

> 50 % 

### 0 0 1 0 4 2 

### 26 1 

# lu 0 0 1 1 6 4 9 "66 " Site s

< 50 % 

### 7 2 3 2 0 

### 1 0 

# I T 9 3 0 11 

### 0 0 "5 0 Tota l



### 7 2 3 3 0 4 3 

### 26 1 36 4 

### 9 3 0 1 2 1 6 4 9 "116 "

8314 6 8• •



### E •--.30000 0 '• • .0; ; 0. 0 0 0 •' :
. 7.00.00. 7 * 1 . 20.0.00 * 1 . 50.00. 7 * 1 . 80.0.0. 7 •'2

. I.O.OJJP J " 2 . 40.0.•..'. 7 : '-2 . AOO0. 7 "3.0.0. 0 0 0

•• • 3 . 30.1.10. 7 •••3 . '.'J.O.0. 7 "3.70.00. 7 * 4 . 20.00. 7

- 4 . 0'0.i.0) 0 * 4 .8.0.0.0. 7 * 5 . 10.00. 7 •5.40.0.0. 7 *5.7.U.0.u. 7 •6.0000 0 •6.3000 0 * 6 . 600.0. 7
• 6 . 70.0.0 0 * 7 . 2.0''0 7 * 7 . 500.0. 7 * 7 . 3.0.00 0 * 3 . 10i'•'•' 7 •3.40.0.7. 7 * 3 . 70,0.0 0 * 9 . "i) u 0 0 * 9 . 30.00 0 * 9.6000 0 * 9 . 9 0. 0 0 0 •10.2.0.0 0 * 10 . 5.00 0 * 10 . 8.00 0 * 1 1 . 100 0 *11.400 0

8314 6 + A A + + + + A + + A + + + + + + + + + + + t
- + + + +
•t
- + + + A + + + + + + + A + + + + • •••• •

SYMBO L COUN T MEA NST.DEV . AOD ) 0.06 0 0.69 1 EAC H SYMBO L REPRESENT S 1 OBSERVATIO N 5 1 0 1 3 2, 7 2 5 3. 7 3 5 4 0 4 5 5 0 5 5 6 0 6 5 7 0 7 5 8 0 •

FREQUCNC Y INT . AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA * 36 0

5 1 0 1 3 2 0 2 5 3 0 3 5 4 0 4 5 5 0 5 6 6 0 6 6 7 0 7 6 8 0 Numbe r o f Site s

FIGUR E B-2a . Histoqra mo f expecte d primar yexceedances , E , fo r thos e site s reportin g one : hou r C O concentration s i n 1981 : SLAM S (includin g NAMS ) 0 0 0 1 0 l 0 0 0 0 0 0 I'l 0 0 0 0 0 ti ti ti ti ti ti 0 1 0 0 ti kl a 0 I ti ti kl 0 CUM . 30 0 3 0 0 3 0 0 30 0 36 1 36 1 •jc:i 30 2 3 0 2 3 '.

. 2 3 0 2 3 0 2 3 6 2 3 0 2 3.' . 2 36 2 76 2 3 0 2 30 2 3 0 2 3-7 2 3 7 2 37 2 3 0 2 3 0 2 3 0 2 3 0 3 8 0 3 3 0 3 J (. 1 z' 8 0 3 8 ( J 3 36 3 3 0 4 30 4 37 4 3 G4 30 4 •

PERC E HIT . 9 8. 9 0. 0 0. 0 0. 0 0. 3 0. 0 a. 3 ti .ti 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 3 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 3 0. 0 0. 0 0. 0 0. 0 NTA^ E CUM . 9 8 . 9 9 8. 9 9 8 . 9 9 8. 9 9 7. 2 99. 2 0 0

. D 9 7. 5 9 3 . 5 9 7. 5 9 7. 5 9 7 . 5 97. 5 97. 5 9 7. 5 9 9. 5 9 7. 5 9 7. 5 9 7. 5 9 7 . 5 9 7 . 5 9 7. 5 9 7. 5 9 7. 5 9 7. 6 9 7 . 5 9 9 . 7 9 7 . 7 9 7. 7 97. 7 9 7 . 7 9 7. 7 9 7. 7 1.7 0 . 0 100. 0 100. 0 1 0 0 . 0 1 0 0 . 0 •P.2 8

u . «^«^««__«^«rt« « —«^-H«^,««««_„^, r t— —- * — — . ~ - < , * ^ <

a 1 5 S ^ 3 CTCTCTCTlCr>CTlCTCTCTlCTlO^CTCTO^CTCTClCTCTlCTO>CTCTCTO%CTCTCTCT0-lCTCriCT^CTCrii^^ t — o o^aiCTio^o^oi^o^^c^o^cri^^o^^Q\Cj^CTO^o%a^a\^o^^^CTCTO^CT^cna>^CT^ ^ z— p « U l (_ ) . . ^ 5

5 B j ^jc-^^^ta^^^^^iaBj^t ^ ^ ^ ^ S 3 ^ ^ va^^^^^BS^C Q C $ ^ ^ ^ CTl ^ cs : i - U JZ CT^^^C^^^^CS^^ 0 ^ ^ ^ i S ^ ^ ^ ^ ^ (^5l^}g5£}£ii5i5j5l^£i^5l^5lB l C ^ 5 } a . »- • a\

>• . O S ininininminmminmminmininmmminmmmminmmmmmmminmmmmiDi o

= 5 

### o • \ • i j

— Lr t& 3 t ^ ^ ^ to. to to to. to to. to. & 5 ) to to. to ti) to i s to to to to to. to to to to to ^ 5 to 5 5 to to to " ^ to.

« M

- . IA • fciz > •o U J— • « D H • < I - > vt a i U l l/ > c a fo Oi ZBj - < • U l S 3

•Z-'OIA 3 —H O - Z O u i IA U l

_ i a i O il e a U l S co Q i

> • (A

_ l o c a > IA OL LJ X I u 1 - < O u i 6 3

C O
i n

i s

L P I C

L O u o

U O

U O

• « •

« s

tn r o

ro

B l C M

B J + « c a c a c a c a c a c a c a c a c a m c a a ea ca ca ca c a c a c a c a c a c a c a c a c a c a c a c a c a c a c a c a c a ca ca e a c a c a c a c a c a c a c a c o =3 c a ca ca ca = i ca ca ca ca ca a> ca ca ca ca ca ca ca ca ca ca ca ca ca ca ca ca ca ca ca co ca ca co + + B l C O

+ i n

+ S 3

+ i n U 3

+ B ! i n

+ tn i n

+ B l i n

+ B i

+ i n r o t o c u +- >

< + - O

< D E 3 l/ l

d l 4- > $ - (1 1

. Q CM I I C O
a:
= 3

+ B l p o

+ t n

+ . B i ( M

+ B l

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + fcaQ&>S3^^C5fi353^C3BlBl^^3BlSJfc!OQt3^£3C3taiS!^Q£P^O^ta^i3Bl^ia GlO^^O&^iSO^OQOQ^OOOQQO^Q^QCJiSO^^O^^&i^^^t S & 1 i ato S O GiQ^Q^QiS&^Q^i^utQ^QOiSlSJQ&IQQQ^OOS ) O ^ B j ^ Bl ^ &25j£iBlBiQO&J^t^QB!^Sl^£j^^5JQ^&lB!SlO^Bi^Q^^BlBJCMinC0~* ^ BispOCMtnc o —nrr«.QroincTiCMinc3-.-«»r^{acou30-icMinc o — •< * r ^ t aro mo

n niOO l .....fc$tiat9-« ^
• • .-»,-_<rp|NN(onnnfnnL'iininioiDioiDr>h.N»coc3!jioMJioi-<rt'" p —
t o j -P.2 9

SLAM S OTHE R N
- 48 0 COR=-.006 8 MEA N X 75.23 4 Y .0679 2 SYMBOL= S

SYMBOL= 0

ST.DEV . 27.01 5 .7794 3 REGRESSIO NLIN E RES.MS .

X=-.23588*Y + 75.25 0 731.2 9 Y=-195E-6*X + .0826 9 .6087 6 ,X . + . 12. 0 +

10. 5 +

9.0 0 +

7.5 0

6.0 0

4.5 0

3.0 0

1.5 0 +

0.0 0 Y* 0 0* * S** 0 0***** * **0*S***0 * SS * * SSS***S*****SSS************** S Y .+....+....+....+....+....+....+....+....+....+....X...

. + ... . + .. . . + ... . + . 7.50 0 22.5 0 37.5 0 52.5 0 67.5 0 82.5 0 97.5 0 0.00 0 15.0 0 30.0 0 45.0 0 60.0 0 75.0 0 90.0 0 105. 0



### FIGUR E B-3 . Scatte r plo t o f the numbe r o f expecte dprimary exceedance s (E ) versu s the percentag e (p ) o f tota l hour s recorde di n198 1 fo r site s reportingone-hou r C Oconcentrations .

8311+ 6TABL E B-3 . Frequencytabl e o f the percentag e o f tota l day s recorde d in 198 1 (p )

versu s expecte d primaryexceedance s (E ) fo r site s reporting 1-hou r CO averages .


## E 0 

### < - 

### n TOTA L 0 -- 26 X

4 6 1 4 7 < = 46.5 X

4 8 0 4 8 < = 71 %

5 0 1 5 1 < - 83 X

4 6 0 4 6 < - 90 X

6 5 1 6 6 

## P
< - 91.5 X

2 8 1 2 9 < - 94 X

5 0 1 5 1 < - 96 X

4 8 0 4 8 O97.5 X< - \B0X

4 3 5 1 1 0 0 1 4 3 5 1 1 TOTA L 47 5 6 48 0

8314 6

## E

* 40.000 0 * 80.000 0 * 120.00 0 * 160.00 0 * 200.00 0 *240.00 0 *280.00 0 •320.00 0 •360.00 0 •400.00 0 •440.00 0
• 4 80.00 0 •520.00 0 •560.00 0 •600.00 0 •640.00 0 •680.00 0
• 7 20.00 0 •760.00 0 •800.00 0 •840.00 0 •880.00 0 •920.00 0 •960.00 0 •1000.0 0 •1040.0 0 •1080.0 0 •1120.0 0 •1160.0 0 •1200.0 0 •1240.0 0 •1280.0 0 •1320.0 0 •1360.0 0 •1400.0 0 •1440.0 0 •1480.0 0 •1520.0 0 SYMBO L COUN T MEA NST.DEV . A36 4 34.30 1 112.44 9 EAC H SYMBO L REPRESENT S 1 OBSERVATIO N 5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5 5 0 5 5 6 0 6 5 7 0 7 5 8 0 +AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA * +AAAAAAAAAAAAAAAAAA A +AAAAAA A +AAAAAAA A +AAA + AAA A + A + AA A +A A +AA + AA A + + + + A + + + + + + + A + + + + + + + + + + + + + A + + + 5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5 5 0 5 5 6 0 6 5 7 0 7 5 8 0 FREQUENC Y INT . 30 9 1 9 7 

### a 3 4 1 3 2 2 3 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 CUM . 30 9 32 8 33 5 34 3 34 6 35 0 35 1 35 4 35 6 35 8 38 1 36 1 36 1 36 1 3G 2 36 2 36 2 36 2 3G 2 36 2 36 2 36 3 36 3 36 3 36 3 3G 3 36 3 36 3 36 3 3G 3 36 3 36 3 36 3 36 3 36 4 3G 4 3G 4 36 4 PERCENTAG E INT . 84. 9 5. 2 1. 9 2. 2 0. 8 1 . 1 0. 3 0. 8 0. 5 0. 5 0. 8 0. 0 0. 0 0. 0 0. 3 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 3 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 3 0. 0 0. 0 0. 0 CUM . 84. 9 90 . 1 92. 0 94. 2 95 . 1 96. 2 96. 4 97. 3 97. 8 98. 4 99. 2 99. 2 99. 2 99. 2 99. 5 99. 5 99. 5 99. 5 99. 5 99. 5 99. 5 99. 7 99. 7 99. 7 99. 7 99. 7 99. 7 99. 7 99. 7 99. 7 99. 7 99. 7 99. 7 99. 7 100. 0 100. 0 100. 0 100. 0
Numbe r o f Site s

FIGUR E B-4a . Histogram o f expecte dprimary exceedances , E , fo r thos e site s reportin g8-hou r C Oconcentration s i n1981 : SLAM S (includin gNAMS ) 8314 6

## E •40.000 0 •30.000 0 •120.00 0 •160.00 0 •200.00 0
• 2 40.00 0 •280.00 0 •320.00 0 •3G0.00 0 •400.00 0 •440.00 0
• 4 80.00 0 •520.00 0 •560.00 0 •600.00 0 •640.00 0 •680.00 0
• 7 20.00 0 •760.00 0 •800.00 0 •840.00 0 •880.00 0
• 9 20.00 0 •960.00 0 * 10.0 0 .0 0 •1040.0 0 •1080.0 0 •1120.0 0 •1160.0 0 * 1200.0 0 •1240.0 0 •1280.0 0 •1320.0 0 •1360.0 0 •1400.0 0 •1440.0 0 •1480.0 0 •1520.0 0 SYMBO L COUN T MEA N ST. DEV . OTHE R B 11 6 28.34 8 90.30 G EAC H SYMBO L REPRESENT S 1 OBSERVATION S 5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5 " 5 0 5 5 6 0 6 5 7 0 7 5 8 0 +BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB6B * + BBB B + B + B + B + + B + B + B + B + B + + + + B + + + + + + + + + + + + + + + + + + + + + + + 5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5 5 0 5 5 6 0 6 5 7 0 7 5 8 0 FREQUENC Y INT . 10 3 4

• 1 0

0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 CUM . 10 3 10 7 10 8 10 9 11 0 1 1 0 1 1 1 1 1 2 11 3 11 4 11 5 11 5 11 5 11 5 11 6 11 6 11 6 11 6 11 6 11 6 11 6 11 6 11 6 11 6 11 6 11 6 11 6 11 6 11 6 1 1 6 11 6 11 6 1 1 6 11 6 11 6 11 6 11 6 11 6 PERCENTAG E INT . 88. 8 3. 4 0. 9 0. 9 0. 9 0. 0 0. 9 0. 9 0. 9 0. 9 0. 9 0. 0 0. 0 0. 0 0. 9 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 CUM . 83. 8 92. 2 93 . 1 94. 0 94. 8 94. 8 95. 7 96. 6 97. 4 98. 3 99. 1 99. 1 99 . 1 99 . 1 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 10. 0 . 0 100. 0 Numbe ro fSite s FIGUR E B-4b

. Othe rsites , 83H4 6P.3 3

140 0 +

122 5

105 0 +

875 .

700 .

525 .

350 .

175 .

0.0 0 SLAM S OTHE R N = 48 0 COR=-.037 6 MEA N X 75.48 2 Y 32.86 2 SYMBOL= S

SYMBOL» 0

ST.DEV . 27.26 0 107.4 6 REGRESSIO NLIN E RES.M S

X=-.00953*Y + 75.79 5 743.6 3 Y=-.14812*X + 44.04 3 1155 5 ,+X

.

OS * YS O +* 0 0* * S*0 0 00****S0*** * *s*so*s s .+....+....+....+....+....+....+.. . S S S SSOSS S S OSSS**OS********S**S*S********* S i-.

. . . + ... . + .. . ,+X

. . . + .. . . + ... . + . . 0 s s s s 0 s s s O S s s s s s s s s s s s s S O S s s o s*s* * s**s s 7.50 0 22.5 0 37.5 0 52.5 0 67.5 0 82.5 0 97.5 0 0.00 0 15.0 0 30.0 0 45.0 0 60.0 0 75.0 0 90.0 0 105. 0



### FIGUR E B-5 . Scatte r plo t o f th e numbe r o f expecte dprimary exceedance s (E ) versu s th e percentag e (p ) o f tota l hour s recorde di n198 1 fo r site s reporting8-hou r COconcentrations .

8311+ 6TABL E B-4 . Frequencytabl e o f the percentag e o f tota l day s recorde d i n 198 1 (p ) versu s expecte d primaryexceedance s (E ) fo r site s reporting 8-hour C O averages .

0 25. 5 < - 46 X< = 70.5 X< - 82.5 X< - 89 X< - 91.5 X< - 94.5 X< - 96.5 X< - 98.5 X< - IBBX TOTA L 0 1 - 5 < - 1 0 < - 5 0 < - 10 0 < - 140 0 TOTA L 3 6

0 1 3 0 6 4 6 3 1

0 5 7 1 5 4 9 3 1

3 3 7 3 3 5 0 2 9

3 6 6 1 3 4 8 2 7

5 4 5 1 4 4 6 3 1

1 3 9 0 2 4 6 2 6

7 5 4 2 6 5 0 2 1

6 5 1 2 0 1 4 5 2 5

5 0 1 5 3 4 5 2 2 1

2 1 1 1 4 9 4 8 27 8

3 2 3 3 7 9 1 5 4 3 4 8 0

8314 6

c o fcP.3 5



### Appendi xC NITROGEN DIOXID E DESCRIPTIVE STATISTIC S

8314 6 5P.3 6



### APPENDI XC NITROGE N DIOXID E DESCRIPTIV E STATISTIC S

Figur e C- 1 show s th e distributio no f dat a completenes s amon gai r monitor ingstations . Ther e ar e lo w correlation s betwee nth e percentag e o f dat a completenes s an

dreporte dannua l averages , standar ddeviations , an d coefficient s o f variatio n(Figur e C-2) .

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.

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,x+ . 7.50. 7 2 2.5 0 3 7.5 0 5 2.5 0 57.5 0 02.5 0 97.5 0 0.00 0 15.0 0 30.0 0 4 5.0 0 60.0 0 75.0 0 90.0 0 105. 0



### FIGUR E C-2a . Scatte r plot s o f ambien t ai r qualit y statistic s versu s th e percentag e o f tota l hour s recorde din198 1 (p ) b y site s reportin g NO? : annua l averag e versu s p .SLAM S , 5YMB0L= S OTHE R , 5YMBOL= 0 N = 32 4 ) COP. = .00 4 4 MEA NST.DEV . REGRESSIONLINE RES.MS . X 54.23 1 23.02 7 X= .0.;792 * Y + 63.9G 5 737.9 G Y 2 7.50 5 13.3-3 5 Y= ..J.J2 i 9")! + 2/.- ! 4 5 193.3 9

,X . + .

0 S O 0 0 0 s o s

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0

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s s 3 0 S S s s s s 0 S Oi
5 S SC O S 0 SSS S "S3 0 SS S 33S353 ; 30S S 0 S VJ-JOSOSS S

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0 0 SO 0 0 0 0 SO O

7.30 0 22.3 0 37.3 0 52.5 0 (57.3. 7 32.5 0 97.5 0 00. 0 15.0 0 30.0 0 45.0 0 C-0.0 0 75.0 0 90.0 0 105. 0

FIGUR E C-2b . Standar ddeviatio nversu s p .P.4 1

SLAM S , SYMBOL' S OlMlc' R , 3YHBO L= 0 N = 32 4 COR--.143 0 ;;EA N ST.DEV

. REG^ZSSIO N LT>;
- iziis.i-ii

. X 61.23 1 23.02 7 X = -27 . 1 39 * Y + 77..0 G//.J./I .3730 4 .2,033 1 Y= - . ...J7.J9* X+ .7427 7 .0;;.', 3

2 . 1

0 0 0 0 0 0 0 0 3 O J 0 0 0 3 SOO' J 0 S z O S 0 0 o •-• •0 S 0005S O S 3 0 CO S 3 -'•J * 0

S O S 3 3 0 0 SS S O O S 0 0 0 0 0 S 0 0 s * s Si'! 3 s *

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. 5. 0 75.0 0 90.0 0 105 ,



### FIGUR E C-2c . Coefficien t o f variatio n(standar ddeviatio n divide db

y annua l average ) versu s p .P.4 2



### Appendi xD OZONE DESCRIPTIVE STATISTIC S

8314 6 5P.4 3

APPENDI XD OZON E DESCRIPTIV E STATISTIC S

Figur e D- 1 show s th e distributio no f dat a completenes s amon gai r monitor in gstations . Histogram s o f "expected " exceedance s (i.e. , \. or k i /p i inth e notatio no f Sectio n2 ) sho

w tha t roughl y

8 5 percen t o f th e station s reporte dseve no r fewe r expecte dexceedance s in198 1 (Figur e D-2) . (Thes e statistic s ar e purel y fo r descriptiv e an dcomparativ e purpose s an ddo no t directl y pertai nt o th e attainmen t o f th e ambien t standard , sinc e thi s i s determine dfro

m thre e year s o f data , no t fro m a singl e year. ) Somewha t greate r expecte dexceedance s wer e reporte db y station s havin gnearl y complet e data ; 4 percen t o f th e station s reporte dzer o expecte dexceed ance s (Figur e D-3 an dTabl e D-1) . Judgin gb y Tabl e D- 1 an

dth e lo

w correlatio ngive ninFigur e D-3 , no clea r relationshi pi s eviden t betwee n expecte dexceedanc s an ddat a completeness .

831»* 6 5* 3. 0000 0 * 6.00000 * 9.0000 0 * 12.000 0 * 15. 000 0 * 10.000 0 00.0 0 000 0 000 0 000 0 000 0 000 0 000 0 *2 1 *24 *2 7 *3 0 *3 3 *3 6 *3 9 *4 2 *4 5 *4 8 *5 1 *54 00.0 0 000 0 000 0 000 0 000 0 * 57.000 0 * 6 0.000. 0 * 6 3 . .000 0 *56.000 0 *69.000 0 * 72.000 0 "75.000 0 * 7 8 .00,0 0 *81.000 0 * 3 4.000 0 * 3 7.000 0 * 90.000 0 * 9 3.000 0 * 96.000 0 * 99.000 0 * 102.00 0 •105.00 0 * 100.00 0 * 1 1 1 .00. 0 * 114.00 0 SYMBO L COUN T A54 8 EAC H SYMBO L REPRESENT S MEA N ST. DEV . 81.76 7 18.52 8 1 OBSERVATIO N 1 0 1 5 2 0 2 5 3 0 3 5 40 4 5 5 0 5 5 6 0 6 5 7 0 7 5 8 0

+AAAAAA A + A + A + AA A + AAA A + A + A A + A A + AA A + AA A +AAAAAAAA A +AAA +AAAAAAA A +AAAAAA A +AAAAAAAA A +AAAA A +AAAAAAAAA A +AAAAAAAAAAAAA A +AAAAAAAA A +AAAAAAAAAA A +AAAAAAAAAAA A +AAAAAAAAAAAAAAAAAAAAAAAAA A +AAAAAAAAAAAAAAAAAAAAAAAAAA A +AAAAAAAAAAAAAAAAAAAAAAAAAAAAAA A +AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA A +AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA A +AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA A +AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA A +AAAAAAAAAAAAAAAA.V.AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA A +AAAAAAAAAAAAAAAAAAAA A

1. 0 2 0 - - + - 3 0 • - + - 3 5 . _+ . 4 0 - - + -

5. 0 5 5 - - + -

6 0 - - + - 6 5 . _+ - 7 0 - - + - 7 5 • - + 8 0 FREQUENC Y INT . CUM . 0 0 0 0 7 1 1 3 4 1 2 2 3 3 9 3 8 7 9 5 1 0 1 4 9 1 1 1 2 2 6 2 7 3 1 5 5 5 1 6 2 7 3 7 8 2 1 0 0 0 0 0 0 0 0 7 8 9 1 2 1 6 1 7 1 9 2 1 2 4 2 7 3 6 3 9 4 7 5 4 6 3 6 8 7 8 9 2 10 1 11 2 12 4 15 0 17 7 20 S 26 3 31 4 37 6 4 4 9 52 7 54 8 54 8 54 0 54 8 5 4 0 PERCENTAG E INT . CUM . 0.0 0.0 0.0 0.0 1 . 3 2 2 5 7 2 4 4 5 5 6 5 5 3 6 9 8 6 G 0 2 7 9 7 10. 0 9. 3 11. 3 13. 3 14. 2 3. 8 0. 0 0. 0 0.0 0.0 0. 0. 0. 0. 0. 0. 0. 0. 0. 1 . 0. 1 . 1 . 1 . 0 . 1 , 2 . 1 , 2 . 2 . 4 , 4 . 5 . 0.0 0.0 0.0 0.0 1 . 3 1 . 5 1 . 2 . 2 . 3 , 3 . 3 , 4 . 4 , 6 . 7 . 8 . 9 , 11 , 12 . 14 . 20 , 22 . 27 . 32 . 16. 8 18. A

38. 0 49. 0 57. 3 68. 6 8 1 . 9 96 . 2 1 00. 0 100. 0 100. 0 100.. 0 100. 0

Numbe r o f Site s

H o , ] i , c , FIGUR E D-la . Histogra mo f th e percentage , p , o f dat a completenes s fo r thos e site s

reportin g 0- , concentration s i n 1981 : SLAM S (includin g NAMS) .* 3.0000 0 * 6.0000 0 *9.0000 0 * 12.000 0 * 15.000 0 * 1 8 .00.0. 0 000 0 000 0 000 0 *2 1 *2 4 *2 7 *3 0 •3 3 *3 6 *3 9 *4 2 *4 5 *4 8 *5 1 *5 4 000 0 000 0 000 0 000 0 000 0 000 0 000 0 000 0 .00.0 0 ' 5 7.000. 0 *G 0 *6 3 *6 6 *G 9 *7 2 *7 5 *7 3 *8 1 * 8 4 *8 7 *9 0 *9 3 *9 6 *9 9 *10 2 *10 5 M 0 B *1 1 1 *11 4 000, 0 000 0 000 0 000. 0 000 0 000 0 000 0 0.00 0 000 0 000 0 000 0 000 0 000. 0 000 0 00 0 00 0 00 0 00 0 00 0 SYMBO L COUN T B 17 5 EAC H SYMBO L REPRESENT S MEA N ST.DEV . 64.90 6 27.49 9 1 OBSERVATIO N 1 0 5 + + + + B + B B + B + +BBBB B + BB B + E U + B B + BBB B +GBGCB B +BBBGBBB B + BBB B + BB B +BBBBB B + G B +BBBB B + BBB B + B + B B + B B +BBBBBBB B +BBBB B + B B +BBBBBBB B +BBBBBBB B +BBBB B +BBBBBBBBB B +BBBBBG B +BBBBB B +BBBBBBBBBB B +BBBBBBBBB8BBB B +CBBSBCEBBBBB B +BBBBBBB3B B +BBBB B + + + + i + + 1 5 2 0 2 5 3 0 3 5 4 0

- - + - 5 0

- - + - 5 5 6 0 6 5 7 0 7 5 8 0

1 0 1 5 - - + -

2 0 - - + -

3 0 - - + - 3 5 4 0 - - + -

4 5 - - + - 5 0 5 5 - - + -

6 0 - - + - 6 5 - - + - 7 0 - - + - 7 5 - - + 8 0 Numbe r o f Site s FREQUENC YPERCENTAG E INT . CUM . INT . CUM . 1 2 1 0 5 3 2 2 4 6 0 4 3 6 2 5 4 1 2 2 8 5 2 8 8 5 1 0 7 6 1 1 1 4 1 3 1 0 5 0 0 0 0 1 3 4 4 9 1 2 1 4 1 6 2 0 2 6 3 4 3 8 4 1 4 7 4 9 5 4 5 0 5 9 6 1 6 3 7 1 7 6 7 8 O G 9 4 9 9 10 9 H G 12 2 13 3 14 7 16 0 17 0 17 5 17 5 17 5 17 5 17 5 0. 6 1 . 1 0. 6 0. 0 2. 9 1 . 7 1 . 1 1 . 1 2. 3

1 . 1 2. 9 0. 6 1 . 7 2. 3 2. 3 5 . 1 6. 9 8 . G f 9. 1 11. 4 14. 9 19. 4 2 1 , 23 , 26 , 2 0. 0 30. 9 2 0 1 1 4 2 1 4 4 2 5 4 3 6 8 7 5 2 0 0 0 0 3 6 1 1 6 9 1 6 6 9 7 0 4 3 0 4 7 9 0 0 0 0 3 3 3 3 3 4 3 6 4 0 4 3 4 4 4 9 5 3 5 5 6 2 6 6 6 9 7 6 0 4 9 1 9 7 1 0 0 1 0 0 1 0 0 10 0 1 0 0 1 7 o 0 G 4 G 1 7 6 3 3 7 0 ti 4 1 ti ti ti ti ti

FIGURE D-lb . Othe r sites ,

8 iM'it
.SYMBO L COUN T A54 0 EAC H SYMBO L REPRESENT S MEA NST.DEV . 7.44 5 22.39 8 1 OBSERVATIO N E 5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5 5 0 5 5 6 0 6 5 7 0 7 5 8 0 + + + + + + + + + + + + + + + + + * 7.0000 0 +AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA * * 14.000 0 +AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA A * 21.000 0 +AAAAAAAAAAA A * 28.000 0 +AAAA * 35.000 0 +AAAAA A * 42.000 0 +A

A * 49.000 0 + A * 56.000 0 +AA *63.000 0 +AAAA * 70.000 0 + * 7 7.000 0 + A *84.000 0 + *91.000 0 + A •98.000 0 +AAA •105.00 0 +AAA •112.00 0 + •119.00 0 +A

A •126.00 0 +AAA •133.00 0 + •140.00 0 +A

A •147.00 0 +AAA •154.00 0 + A •161.00 0 + •168.00 0 + •175.00 0 + •182.00 0 + •189.00 0 + •196.00 0 + •203.00 0 + •210.00 0 + •217.00 0 + •224.00 0 + •231.00 0 + •238.00 0 + •245.00 0 + •252.00 0 + •259.00 0 + •266.00 0 +

+ + - FREQUENC Y PERCENTAG E INT . CUM . INT . CUM .

• - + - 1 0 • - + - 1 5 • - + - 2 0 - - + - 2 5 - - + - 3 0 - - + -

4 0 - - + -

5 0 - - + - 5 5 - - + - 6 0 - - + - 6 5 - - + - 7 0 - - + - 7 5 - - + 8 0 46 3 3 5 1 2 4 6 2 1 2 4 0 1 0 1 3 3 0 2 3 0 2 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 46 3 49 8 51 0 51 4 52 0 5 2 2 52 3 52 5 52 9 52 9 53 0 53 0 53 1 53 4 53 7 53 7 53 9 54 2 54 2 5 4 4 54 7 54 0 54 0 54 0 54 0 54 0 54 0 54 0 54 0 54 0 54 0 54 0 54 8 54 8 54 8 54 8 54 0 54 8 34 . 6 . 2 . 0. 1 , 0 . 0 , 0 . 84. 5 90 . 9 93 . 1 93. 8 94. 9

0. 7 0. 0 0. 2 0. 0 0. 2 0. 5 0. 5 0. 0 0. 4 0. 5 0. 0 9 5 9 5 9 5 9 6 9 6 9 6 9 6 9 6 9 7 98. 0 98. 0

0 . 0 . 0 . 4

. 5

. 2 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 98. 4 98. 9 93. 9 99. 3 99. 8 100. 0 100. 0 100. 0 100. 0 100. 0 100 .0 1 00

. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0

Numbe r o f Site s

(314 6 FIGUR E D-2a . Histogra

mo f th e numbe r o f expecte d primar yexceedance s (E ) for site s reporting 0 3 concentration s in 1981 : SLAM S (includin g NAMS )

## E
* 7 .0000. 0 •14.000 0 •21.000. 0 •28.000 0 •35.000 0
• 4 2.000 0 •49.000 0 •56.000 0 •63.000 0 * 70.000 0 •77.000 0 •84.000 0 •91.000 0 •90.000 0 •105.00 0 •112.00 0 •119.00 0 •126.00 0 •133.00 0 •1 4 0.00 0 •14 7.00. 9 •1 5 4.00 0 *161.00 0 •168 . OD.-

J •1 7 5.0.0 0 •1 8 2.00 0 •1 8 9.00 0 •19G.00 0 •203.00 0 •210.00 0 •217.00 0 •224.00 0 •231.00 0 •238.00 0 •245.00 0 •252.00 0 •259.00 0 •266.00 0 SYMBO L COUN T MEA NST.DEV . B 17 5 9.38 3 35.65 7 EAC H SYMBO L REPRESENT S 1 OBSERVATIO N 5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5 5 0 5 5 6 0 6 5 7 0 7 5 8 0 +B3BBBBBBBBBBBBBB 5 3BBBBBBB3BBQBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB8BBBBBBBBBBBBB * +BBBBB3BB B +BBBB B + B B + BB B + B + B B + + + + + B + + + + + + B + + + + + + + + + B + + + + B + + + B + + + B + 5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5 5 0 5 5 6 0 6 5 7 0 7 5 8 0 FREQUENC Y INT . 14 7 9 5 2 3 1 2 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 1 0 CUM . 14 7 15 6 16 1 16 3 16 6 16 7 16 9 16 9 16 9 16 9 16 9 17 0 17 0 17 0 17 0 17 0 17 0 17 1 17 1 1 7 1 17 1 17 1 17 1 17 1 17 1 17 1 17 2 17 2 17 2 17 2 17 3 17 3 17 3 17 4 17 4 17 4 17 5 17 5 PERCENTAG E INT . 84. 0 5 . 1 2. 9 1 . 1 1 . 7 0. 6 1 . 1 0. 0 0. 0 0. 0 0. 0 0. G 0. 0 0. 0 0. 0 0. 0 0. 0 0. G 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 6 0. 0 0. 0 0. 0 0. G 0. 0 0. 0 0. G 0. 0 0. 0 0. G 0. 0 CUM . 84. 0 89 . 1 92. 0 93 . 1 94. 9 95. 4 96. 6 96. 6 96. 6 96. 6 96. 6 97 . 1 97 . 1 97 . 1 97 . 1 97 . 1 97 . 1 97. 7 97. 7 97. 7 97. 7 97. 7 97. 7 9 7. 7 97. 7 97. 7 90. 3 90. 3 90. 3 98. 3 93. 9 90. 9 9 0. 9 9 9. 4 9 9. 4 99. 4 100 . ti 1 0 0 . ti
Numbe r o f Site s
FIGUR E D-2b . Othe r sites
. p . ( ,P.48

SLAM S OTHE R M - 72 3 COR - .007 3 MEA N X77.68 5 Y7.915 4 SYMBOL= S

SYMBOL= 0

ST.DEV . 22.23 8 26.21 4 REGRESSIO N LIN E X = .00621*Y + 77.63 6 Y = .00863*X + 7.245 2

. . + +..X. + + . . RES.MS . 495.1 9 688.0 8

28 0

24 5

21 0

17 5

14 0

10 5

70 .

35 .

0. 0 O O S S S s s s s SS S s s s s o s
S S S s s s s s s s s s s 0 s ososss s s s s s ssssss*s«s*s*ss s 

### 0 s 0 • O S

s s o 0 0 OO S 0 S S SO S s s Y0 SSOO Os s * s s**o s ssssss*s*s*s*ss s Y + 0 0 0 **o*o* o s* * ** s o****ss*s*ss * **«**s*S********************* s + .+....+....+....+....+....+....+....+....+....+....+.X..•....+....+....• . 7.50 0 22.5 0 37.5 0 52.5 0 67.5 0 82.5 0 97.5 0 0.00 0 15.0 0 30.0 0 45.0 0 60.0 0 75.0 0 90.0 0 105. 0

FIGUR E D-3 . Scatte r plo t o f th e percentag e o f dat a completenes s i n198 1 (p ) versu s th e numbe r o f estimate dprimar y exceedance s (E ) fo r site s reportin g0 3 concentrations .

8314 6TABL E D-1 . Frequencytabl e o f the percentag e o f tota l day s recorde d in 198 1 (p )

versu s expecte d primaryexceedance s (E ) fo r site s reporting 1-hour ozon e averages .

8314 6 0 - 41 * < = 62.5 X< » 75 X< - 81.5 X 86X 89 X < - 92 X < - 95 X< - 97.5 X< - 100 X TOTA L 0 1 - 2 < ^ 5 <--25 5 10 1 AL 5 1 ja r 9 1 2 7 2 4 1

G 5 2 0 7 1 4 2

a 1 0 1 4 7 5 4 3

1 0 7 1 0 7 0 3 6

1 2 1 6 9 7 3 2 9

ii. 1 3 9 6 3 3 4

1 2 1 6 1 7 7 9 3 7

1 4 1 6 11 7 8 2 5

1 2 8 2 0 6 5 3 0

1 3 1 5 1 9 7 7 36 8

9 9 11 5 14 1 72 3P.5 0



### Appendi xE LEA DDESCRIPTIVE STATISTIC S

8314 6 5P.5 1



### APPENDI XE LEA DDESCRIPTIV E STATISTIC S

Figur e E- l show s th e distributio no f dat a completenes s amon gai r monitor ingstations . Fo r SLAMS , th e mod e o f th e dat a completenes s distributio n i s inth e interva l o f 15-1 8 percent , correspondin gt o nearl y complet e dat a recovery fro m a once-per-six-da y

samplin gschedule . Ther e ar e lo w correlation s betwee nth e percentag e o f dat a completenes s an dreporte d annua l averages , standar ddeviations , an dcoefficient s o f variatio n (Figur e E-2) .

8314 6 5EA C SYMBO L COUN T

A17 1 i l SYMBO L REPRESENT S MEA N ST. DEV . 11.91 7 10.05 9 1 OBSERVATIO N

.0000 0

. 000.0 0 ,0000 0

, 000 0

. 000 0

, 000 0

. 000 0

. 000 0

. 000 0 •30.000 0 *33.000 0 .000 0

, 000 0 .000 0

, 000 0 .030 0 '3 . l G . '9 , '12 . '1 ! M 3 , *2 1 . "24 , '27 . '36 . '39 . '42 , = 45 . '40 , •51.000 0 •54.000 0 *5 7 *G 0 *G 3 •6 6 •6 9 •7 2 . 000 0

. 0.C f 0 .000 0 .000- ; .0.0.0 0

. 00.0 0 •7 5 •7 0 •3 1 *G 4 •8 7 •9 0 "9 3 •9 6 0.00. 7 0 0 0 0 00.0 0 000 0 000 0 000; / 000 0 00 0 0 *99.000 0 * 102.0,0 0 10 5 10 0 11 1 11 4 00 0 00 0 00 0 00 0 5 +AAAAA A +AA + AA A +AAAA A +AAAAA A +AAAAA A + AA A + + + A + A + A A + + + + + + + + + + + + + + + + + + + + A + + + + + + 1 0 1 5

AAAAAAAAAAA/ 1

-\AAAAAAAAAA," ••.AAAAAAAAAA, ' 2 0

.AAAA, '

,A 2 5 3 0 3 5 AAAAAAAAAAAAA .

.AAAAAAAAA . 4 0 4 5 5 0 XAAAAAAAAAAA A

\AAAAAAAAAAAAA A 5 5 6 0 6 5 7 0 7 5 8 0

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA * INT . 4 9 2 3 5 1 9 B 5 3 0 0 1 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 CUM . 4 9 5 1 5 4 5 9 7 8 1G 3 1G 6 1G 6 16 6 16 7 1G 8 17 0 17 0 17 0 17 0 17 0 17 0 17 0 17 0 17 0 17 0 17 0 17 0 170 . 17. 0 17 0 17 0 17 0 17 0 17 0 17 0 17 1 17 1 17 1 17 1 17 1 17 1 17 1 INT . 28. 7 1 . 2 1 . 0 2. 9 11. 1 49. 7 1 . 0 0. 0 0. 0 0. 6 0. 6 1 . 2 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0 . 6 0. 0 0. ti 0.0 0. 0 0. 0 0. 0 CUM . 28. 7 29. 8 31. 6 34. 5 45. 6 95. 3 97. 1 97. 1 97 . 1 97. 7 90. 2 99. 4 99. 4 99. 4 9 9. 4 99. 4 99. 4 99. 4 99. 4 99. 4 99. 4 9 9. 4 99. 4 99. 4 99. 4 9 9. 4 99. 4 99. 4 9 9. 4 93. 4 99. 4 \titi.ti 100 . ti 1 0 0 . 0 1 0 0 . ti 100. 0 100 . ti \titi,ti 1 0 2 0 3 0 3 5 4 0 4 5 5 0 Numbe r o f Site s 6 0 7 0 7 5 0 0

FIGUR E E-la . reportin g Pb Histogra mo f th e percentage , p , o f

in 1981 : SLAM S (includin g NAMS) . dat a completenes s fo r thos e site s

fni'ir ,

### p

* 3 . .0000 0 •6.0000 0 •9.0000 J •12.000 0 •15.000 0 •13.000 0 •21.000 0 •24.000 0
• 2 7.000 0 •30.000 0 •33.0.90 0 •36.000 0 •39.000 0
• 4 2.000 0 •45.00.0 0
• 4 8.000 0 •5 1 .00.0. 7 •54.000 0 •57.000 0 •50.070. 7 •63.000. 7 * G6 . 000, 7 •69.0,00 0 •7 2 .00.0 0 •75.0,i.'0, 7
• 7 8.000 0 •81.000 0

• 3 4 Cf-.-rvyj
• 8 7.000. 7 •90.000. 7 •93.000 0 •96.000 0 •99.000 0 •102.00 0 •105.00 0 •103.00. 7 •HI . 00. 7 •114.00 0 SYM30 L COUN T MEA NST.DEV . B 52 5 7.G1 4 7.02 3 EAC H SYMBO L REPRESENT S 1 OBSERVATIO N 5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5 5 0 5 5 6 0 6 5 7 0 7 5 8 0 +BBBBBEBBBBBBBBBBB3BBBBB3BBBBBBBBBBBBB3B3BBBBBBB3BBBBBBBBB3BBBBBBBBBBBBBBBBBBBCBB * +GBECBBBBBBBBBOB B +B3BEBBBUBBBBBBBB63BBBBDBBBBBBBBBBBBBBSEB3BBBBBBBBBBBBBBBBBBBBBBBBBB5BBBBBBBBBBBB * + BB C + BC BBBE-3' r -BBBBEBBCF'-iBBCBE.!}BBBBBBBB B BBBBBBBBBBBBBBJ-BBCBCBBBBBBBBBB B +BBBBBB 5 3BBBBBBBB 3 3BBB 3 3BUBD33BC3BBBBBBBBBBBBBDB3BBBBCBBBBBBBBBBBBBBBBBBBBBBB3B3B * + +
• + B + B + B + + + + + + + + + + + + + + + + + + + + + + + + + + 5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5 5 0 5 5 6 0 6 5 7 0 7 5 8 0 FREQUENC Y INT . 21 2 1 6 9 5 3 3 3 4 13 2 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 CUM . 21 2 22 0 32 3 35 6 39 0 52 2 52 2 5 2 2 5 2 2 5 2 3 52 4 5 2 5 52 5 5 2 5 L- 2 5 52 5 5>! 5 5 2 5 52 5 5 2 5 5 2 5 52 5 52 5 52 5 5 2 5 5 2 5 5 2 5 52 5 5 Z 5 52 5 5 2 5 52 5 52 5 52 5 52 5 5 2 5 52 5 52 5 PERCENTAG E INT . 40. 4 3. 0 18 . 1 6. 3 6. 5 25 . 1 0. 0 0. 0 0. 0 0. 2 0. 2 0. 2 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 CUM . 40. 4 43. 4 61. 5 67. 8 74. 3 99. 4 99. 4 99. 4 9 9. 4 99. G S 9 . 0 1 0 0 . 0 1 0 0 . ii 1 00

. ti 10.7 . ti 100 . ti 1 0 0 . ti i0H.fi 1 00

. ti 1 00. 0 1 0 0 . ti 1 0 0 . ti 1 0 0 . 0 100. 0 l'XJ.ti 1 0 0 . 0 100. 0 100./: ' 10.7 . ti 1 yj ti . ti 100.0 ltiti.ti 1 titi. ti 1 titi. ti 1 titi. ti 1 00

. ti 1 '7.7 . ti 1 titi. .'. 1
Numbe r o f Site s
FIGUR E E-lb . Othe r sites ,

H ''• I ' i l .P.5 4

SLAM S OTHE R N - 69 6 COR=-.008 0 MEA N X8.671 1 V .4457 8 SYMBOL- S

SYMBOL- 0

ST.DEV . 8.085 0 .6763 9 X

Y REGRESSIO N

LIN E RES.MS . =-.09538*Y + 8.713 6 65.45 7 =-668E-6*X + .4515 7 .4581 4

12. 0 +

10. 5

9.0 0

< . 7.5 0 + O i a . J D a. c t o c u 6.0 0 +

4.5 0

3.0 0 +

1.5 0 + S •S

0.0 0 SS O S 0 0 • SS * s* * 0 • 0 0 0 0 * * *00*0***** S **oo**o****** s **oo*oo*o*o* * + + x. . . + + , s s O S *

. + . 7.50 0 22.5 0 37.5 0 52.5 0 67.5 0 82.5 0 97.5 0 00 0 15.0 0 30.0 0 45.0 0 60.0 0 75.0 0 90.0 0 105. 0

FIGUR E E-2a . Scatte r plot s o f ambien t ai r qualit y statistic s versu s th e percen t (p ) o f dat a completenes s fo r site s reportin gP bconcentration s i

n 1981 : maximu

m quarterl y

averag e versu s p .

8 31 4 6P.5 5

10.0. 7 +

3.75, 7 +
7.50, 7 +

S G.25. 7 +

.ti~ti +

5.7 + , X...

. + ... . + .

3.75. 7 + S

2.5.7 0 + S 0 0 s S OS 3

O O O • 0 0 OS O • ' 5 0 0 SS* " * OOOO3 * ""•••••S Y-*00'-0'--S*- ; '--- S ti.tiZti +-000 " -O-O--*-'"
- SLAM S OT;-:E R , SYMBOL' S

, SYMBOL= 0 N
- 67 0 COR=-.006 8 f i E AN 0.973 4 .31G1 0 ST.DEV . REGRESSIONLINE RES.MS .

8.0'.-..' l X = -.08129*Y + 8.999 1 65.54 5 .6763 0 Y = -559E-G*:; + .3212 0 .4507 2

• S 7.50, 3 22.5 0 37.5 0 52.5 0 67.5 0 82.5 0 97.5 0 Z.utiti 15.0 0 30.0 5 45.0 0 60.0 0 75.0 0 90.0 0 105. 0

FIGUR E E-2b . Standar ddiviatio nversu s p .

3i> eP.5 6

SLAM S OT.IE R N = 67 0 COR=-.025 2 I1EA M X8.973 4 Y.5431 1 SVMBOL= S

SYMBOL= 0

ST.DEV . 0..u9.; i .3121 3 RECESSIO NLIN E RCS.; ; 3 .

X=-.S522S*Y * 9.332 9 6 5.5.-J 7 Y- • - 9 7 1 E - 5 "••";(

+ .5510 2 .0973 4

C 0 E F F V A R 3. 2 +

2. 8

2 . 4

2. 0

1 . 6

1. 2

,3 0

4 0

0. 0 0 0 + S 0

0 0

+ '• * 0 0 S
• 0 0 0 0 s 0 * s s o s s 0 S 0 S 0 0 * * 0 OOOO O OCO'-' S 0 00 : --"- ; " S * soo - ••••'••••••-• s
* OOOOO O o"••• * '•• 0 rt oo o *•••»* *

- 0 OOOOO * *••
- + * OOuOOOO * •••••• • * 0 oo- :
- o o----* * s 00 0 0 0 * 0 0 s s * 0 0 0 0 0 +....+x...+....+ . 0 s s S 0

7.50. 7 22.5 0 37.5 0 52.5 0 67.5 0 82.5 0 97.5 0 0 0 0 15..7 0 30.0 0 45.0 0 60.0 0 75.0 0 90.0 0 105. 0

FIGUR E E-2c . Coefficien t o f variatio n (standar d deviatio n divide d b y maximu mquarterl yaverage ) versu s p .P.5 7



### Appendi xF TS P DESCRIPTIVE STATISTIC S

8314 6 5P.5 8

APPENDI XF TS P DESCRIPTIV E STATISTIC S

Figur e F- l show s th e distributio no f dat a completenes s amongai r monitor in gstations . The mod e o f th e distributio ni s th e interva l o f 15-1 8 percen t dat a completeness , correspondin gt o nearl y complet e dat a recovery fro m a once-per-six-da y samplin gschedule . Tabl e F- l show s th e numbe r o f site s meetin ga

nannua l quot a o f 7 5 percen t dat a completenes s an dmeetin g a correspondin gquarterl y quot a ina specifie dnumbe r o f quarters . Tabl e F- 2 show s th e same informatio nfo r a nannua l quot a o f 5 0 percent . Not e tha t roughl y

7 0 percen t o f th e site s tha t me t a nannua l quot a als o me t a correspondin gquarterl y quot a ineac ho f th e fou r quarter s o f 1981 .
Ther e ar e lo

w correlation s betwee nth e percentag e o f dat a completenes s and reporte dannua l averages , standar ddeviations , an dcoefficient s o f variation(Figur e F2) . Histogram s o f "expected " exceedance s (i.e. , \- or k./p . inth e notatio no f Sectio n2 ) sho

w tha t roughl y

9 5 percen t o f th e station s reporte deigh t o r fewe r expecte dexceedance s in198 1 (Figur e F-3) . Th e greates t numbe r o f expecte dexceedance s wer e reporte db y th e station s havin grelativel y les s complet e data ; 9 1 percen t o f th e station s reporte dzer o expecte dexceed ance s (Figur e F- 4 an dTabl e F-3) . Judgin gb y Tabl e F- 3 an

dth e low correlatio ngive ninFigur e F-4 , no clea r relationshi pi s eviden t betwee n expecte dexceedance s an ddat a completeness .

8314 6 5•

8 3 1 'i f •



### P •3.0000 0 •6.0000 0 •9.0000 0 •12.000 0 •15.000 0 •18.000 0 * 2 1.000 0 •24.000 0
• 2 7.000 0 •30.000 0 •33.000 0 •36.000 0 •39.000 0 •42.000 0
• 4 5.000 0 •48.000 0 •51.000 0 •54.000 0 •57.000 0 •60.000 0 •63.000 0 •66.000 0 •69.000 0 •72.000 0 •75.000 0 •78.000 0 •81.000 0
• 8 4.000 0 •87.000 0 •90.000 0 •93.000 0 •96.000 0 •99.000 0 •102.00 0 •105.00 0 •108.00 0 •111.00 0 •114.00 0 SYMBO L COUN T MEA NST.DEV . A229 5 15.72 2 9.71 8 EAC H SYMBO L REPRESENT S 1 OBSERVATIO N FREQUENC Y 5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5 5 0 5 5 6 0 6 5 7 0 7 5 8 0 IN T +AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA A4 0 +AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA A7 6 +AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA A* 8 6 +AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA * 16 5 +AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA A* 53 4 +AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAM24 0 +AAAAAAAAAAAAAAAA A1 7 +AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA A4 7 +AAAAAAAA A9 +AAAAAAA A8 +AAAAAAAAAAAAAAAAAAAAAAAAAA A2 7 + 0 +A A2 +A A2 + A1 + AA2 + A A2 + A1 + A1 + 0 + A1 + A1 +AAAAA A6 + AA A3 + 0 + A1 +AAAA4 + A1 + 0 +A A2 +AAAA4 +AAAAAA A7 + AAA3 +A A2 + 0 + 0 + 0 + 0 5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5 5 0 5 5 6 0 6 5 7 0 7 5 8 0 Numbe r o f Site s FIGUR E F-la . Histogra mo f th e percentage , p , o f dat a completenes s fo r thos e site s reportin g TS P concentration s i n 1981 : SLAM S (includin g NAMS) . CUM . 4 0 11 6 20 2 36 7 90 1 214 1 216 9 220 5 221 4 222 2 224 9 224 9 225 1 225 3 225 4 225 6 225 8 225 9 226 0 226 0 226 1 226 2 226 8 227 1 227 1 227 2 227 6 227 7 227 7 227 9 228 3 229 0 229 3 229 5 229 5 229 5 229 5 229 5 •

PERCENTAG E INT . 1 . 7 3. 3 3. 7 7. 2 2 3. 3 54. 0 JB T . 7 2. 0 0. 4 0. 3 1 . 2 0. 0 0 . 1 0 . 1 0. 0 0 . 1 0 . 1 0. 0 0. 0 0. 0 0. 0 0. 0 0. 3 0 . 1 0. 0 0. 0 0. 2 0. 0 0. 0 0 . 1 0. 2 0. 3 0 . 1 0 . 1 0. 0 0. 0 0. 0 0. 0 CUM .

1 . 7 5 . 1 8. 8 16. 0 39. 3 93. 3 9 4 .BS 96 . 1 96. 5 96. 8 98. 0 98. 0 98 . 1 98. 2 98. 2 98. 3 98. 4 98. 4 98. 5 98. 5 98. 5 98. 6 93. 8 99. 0 99. 0 99. 0 99. 2 99. 2 99. 2 99. 3 99. 5 99. 8 99. 9 100. 0 100. 0 100. 0 100. 0 100. 0 •* 3.0000 0 * 6.000.0 0 * 9.0000 0 •12.000 0

, 000 0

, 000 0

. 000 0

. 000 0 •27.000 0 * 30.000 0 •33.000 0 •36.000 0 •39.000 0 '15 . '18 . '2 1 '24 .

. 000 0

, 000 0

. 000 0

. 000 0

. 000 0 •57.000 0 •60.000 0 .00.0. 0 '42 . '45 . '48 . '5 1 . '54 . •6 3 •G G •6 3 •7 2 •7 5 •7 8 •3 1 •84 •3 7 •9 0 •9 3 •9 6 •9 9 •10 2 •10 5 •10 8 •11 1 00>z 0 00.0 0 000 0 000 0 000 0 000 0 000 0 000 0 000 0 000 0 000 0 0.0.7 0 00 0 00 0 00 0 00 0 EAC H SYMBO L COUN T B 137 2 SYMBO L REPRESENT S MEANST.DEV . 15.84 5 15.56 3 1 OBSERVATIO N 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5 5 0 5 5 6 0 6 5 7 0 7 5 8 0 FREQUENC Y PERCENTAG E INT . CUM . INT . CUM . + - + B + B + B + B + B + B + B + B + B + B + B + B + B + B + + 3 + B + B + B + B + B + D + B + B + B + B + B + B + B + B + B + B + B + B BBBB3BBBBBBBBBBBBBBBt-BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB * BBBB3BBBBBBBBBBBBBBBCBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB * 6BBBBBBBBBBBBBBBBBBBOBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB * BBBBBE3BBBBBBB3BBBBBBBr,BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBEBBBBBBBBB * BBBB3BBBBBBBBEBBBBBBBBGBBBBBBBBBBBBBBBBBBBBBUBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB * BBBBSBBBBBBBBB3BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB3BBBBBBBBBBBBBBB * BBBBBBBBBBBB B BBBB3BBBBBBBB B BBBBBBBBBB B BBBBBBBBBBBBBBBBBBBBBBBBBB B B B B B B B B B B B B B B B B B B B B B B B B B B B B B BBBB B BBB B B BB B BBB B BB B B B BBBBB B BBBBB B BBBBB B B BB B BBBBBB B

•114.00 0 +

- + - 1 0 • - + - 1 5 - - + - 2 0 2 5 - - + -

3 0 - - + - 3 5 - - + - 4 0 - - + - 4 5 5 0 - - + -

5 5 - - + - 6 0 • - + - 6 5 - - + - 7 0 - - + - 7 5 - - + 8 0 8 7 14 5 13 5 11 9 25 9 46 8 1 6 1 7 1 4 3 3 0 3 1 3 0 3 6 1 5 1 2 4 5 4 2 2 7 7 7 2 1 4 8 1 0 0 0 0 8 7 23 2 36 7 45 6 74 5 121 3 122 9 124 6 126 0 126 3 129 3 129 6 129 7 130 0 130 0 130 3 130 9 131 0 131 5 131 6 131 8 132 2 1 3 2 7 133 1 133 3 1 3 3 5 134 2 134 9 135 6 135 8 135 9 136 3 137 1 137 2 137 2 137 2 137 2 137 2 6 . 10 . 9 . 8 . 18 . 34 . 1 . 1 . 1 . 0 . 2 . 0 . 0 . 0. 2 0. 0 0. 2 0. 4 0 . 1 0 . 0 . 0 . 1 0. 3 0 . 0 . 0 . 0 . 1 0. 5 0. 5 0. 5 0 . 1 0 . 1 0. 3 0. 6 0 . 1 0. 0 0. 0 0. 0 0. 0 6 . 16 . 26 . 35 . 54 , 83 . 89 . 90. 8 9 1 . 8 92. 1 94. 2 94. 5 94. 5 94. 8 94. 8 95. 0 95. 4 95 . 95 . 95 . 96 . 96 . 96 . 97. 0 97. 2 9 7. 3 97. 8 98. 3 98. 8 99. 0 99 . 1 99. 3 99. 9 100. 0 107. 0 100. 0 100. 0 100. 0
Numbe r o f Site s FIGUR E F-lb . Othe r site sP.6 1

TABL E F-l . Numbe r o f site s recordin g 24-hou r TS P durin g a t leas t 7 5 percen t o f th e schedule d monitorin g day s i n 1981 , b yth e numbe r o f quarter s i n whic ha t leas t 7 5 percen t o f th e schedule d day s pe r quarte r ar e recorded .

Sit e Typ e
SLAM S (include s

Othe r No . o f Quarter s

wit ha t Leas t 75 % Days/Quarte r 

### 0 NAMS ) 1



### 2 3 4 Tota l 

### 0 1 2 3 4 Tota l No . o f

> 75 % 

### 0 2 6 9 

### 45 2 133 5 185 8 

### 0 0 7 0 

### 18 5 59 0 845 " Site s

< 75 % 

### 7 9 

### 14 8 14 5 6 5 0 43 7 

### 16 0 20 4 11 9 4 4 0 52 7 Tota l



### 7 9 

### 15 0 21 4 51 7 133 5 229 5 

### 16 0 20 4 18 9 22 9 59 0 137 2

TABL E F-2 . Numbe r o f site s recordin g 24-hou r TS P durin g a t leas t 5 0 percen t o f th e schedule d monitorin g day s i n 1981 , b yth e numbe r o f quarter s i n whic ha t leas t 5 0 percen t o f th e schedule d day s pe r quarte r ar e recorded .

Sit e Typ e
SLAM S (include s

Othe r 

### No .

w i 

### 50 %

NAMS ) o f Quarter s t ha t Leas t Days/Quarte r 

### 0 1 2 3 4 Tota l 

### 0 1 2 3 4 Tota l No . o f

> 50 % 

### 0 0 5 2 

### 23 5 182 5 211 2 

### 0 3 6 2 

### 17 5 78 5 102 5 Site s

< 50 % 

### 3 2 8 9 6 1 

### 1 0 "183 " 

### 7 5 

### 18 6 8 3 

### 3 0 

# W Tota l



### 3 2 8 9 

### 11 3 23 6 182 5 229 5 

### 7 5 

### 18 9 14 5 17 8 78 5 137 2

8314 6 8P.62

SLAM S OTHE R SYMBOL= S

SYMBOL= 0 N = 365 4 COR=-.012 2 MEA N 15.82 1 56.52 6 ST. DEV .

12.22 0 20.82 7 REGRESSIO N

X=-.00714*Y + Y=-.02074*X + LIN E

16.22 5 55.85 4 R E S.MS .

149.3 4 433.3 1

o i 3 . Q 0 0 c t o o i

E O O l 4 0 0

3 5 0

3 0 0

25 0

2 0 0

15 0

10 0

5 0

.

0 . 0 ,X+

.

0 S S s *s * s o * s s** s o oo*o*s * o o*s * + S *0S**S**** 0

* * * * 2 Aft Aft A2 Q Q pp. - * '.-. - ft ft ft * rt rt ft
* V; -.' : rt ft ft * * ft ft rt * YO***"**-"******Q S S*OS*0 0 + Qftrtft:'c*rtrtft**rtftCrtrt * * C i t ft ft Q C
g Q ft ft ft I-

ft ft * rt * * ft ft Q ft ft ft Q 0 * S 0 .0**'"0******* S 0 00 0 0 0

. 0'- * *o**** * 0 0 0 0 0* * c

. * 0 0 0 * S 0 S 0 + 0

. + ... . + ....+X

. . . + .. . . + .... H s s s s sss s SO S * S * s** s O S 3
s 0 0 0 0 0 0 s 0 0 • 0 0 s s SO S s s s »oo s 0 s o 3 00 0 0 0 0 0 s

0 0 s SO S 0 0 0 0 0 00*00 0 0 0 s s o

s « S OO S sss s s o • 0 0 0 0

7.50 0 22.5 0 37.5 0 52.5 0 67.5 0 82.5 0 97.5 0 0.00 0 15.0 0 30.0 0 45.0 0 60.0 0 75.0 0 90.0 0 105. 0

FIGURE F-2a . Scatte r plot s o f ambien t ai r qualit ystatistic s versu s the percen t o f dat a completenes s (p ) fo r site s reporting TS P concentration s in 1981 : annua l geometri c mean versu s p .

8314 6P.63

SLAM S OTHE R SYMBOL= S

SYMBOL= 0 N - 365 4 COR = .079 7 MEA N 15.82 1 1 .5G7 0 ST.DEV .

12.22 0 .2220 0 REGRESSIO N

X= 4.3S43-- Y + Y = .0014 5 * X +

LIN E 8.951 1 1.544 1 RES.MS .

148.4 2 .0459 9

5. 0

4. 5

4. 0

3. 5 +

3. 0 +

2. 5 +

2. 0 +

1. 5 Y *

1 . 0 + 0 ,+X ,

S O
S 0 0 0 0 S 0

0 0 co o 0 OO S • 0 * * 0 0 * 0 0 0 0 * * S0S00*3 a QpV S O ftftp*. V QftS-O*** *
* fcQ rt ft:•;

: ti V

* * :• : A* ; •/ : * -.' : S * * - * * * * * *
* ;" : •/ ; rt

* * rt : t * :\
. V Ki'ftA S Qrt V: fc * -A

' -ii•- * o*os o 0 * 0 +....+.. . 7
. 50 0 00 0 1 s s

s s s s s s *o s ft* rt
* ft ft ft ft rt ft* ft
* rt ft ftft rt ft ft rt
* * ft

x+ , 0 0 0

0 0
SS O 00 0 S O 0 • s o 0 so«* s * 0 0 0 •oos s ooco s * ••'•S O S* S ' SS*S*S*S0**0 S ft*ft*OOOS**S ft* S * Q O S

0 0 s 0 S 0 * SS O s 0 s 0 0 0 0 0 0 0 0 0 0 00 0 •oso s s o s s s s 0

OOO O oosoo o 0 S 0 0 s s • 0

oso s *os s s s s *o s s s s s s S O 0

,+....+....+....+....+....+....+....+....+....+....+....+ . 22.5 0 37.5 0 52.5 0 67.5 0 82.5 0 97.5 0 5.0 0 30.0 0 45.0 0 60.0 0 75.0 0 90.0 0 105. 0

FIGUR E F-2b . Geometri c standar ddeviatio nversu s p .

i 4 6P.6 4

SLAM S OTHE R N° 365 4 COR = .008 2 SYMBOL= S

SYMBOL= 0

MEA N 15.82 1 .0326 2 ST.DEV .

12.22 0 .0393 1 REGRESSIO N

X = 2.52S4*Y + Y = 268E-7*X + LIN E R E S.MS .

15.73 9 149. 3 5 .0321 9 .0015 9 .*....+

. 2 . 4

2
. 1

1. 8 +

C 0 E F F V A R 1. 5 +

1. 2 +

. 9 0

, 6 0

.30+ 0 S

0 OS 0 * OO S 0* S 00'*** * OS O 00 0 0 0 0 0 YO*********x.i*rt***0*o**fto * 0 0 OOO O* 0 *0 0 00*00 0 00*00 0 0 O S * 0 Y 0. 0 +*rt****ftftftft*ftftft**ft5*s**.< o S S * S**'S S 0 0 SOS S S O S S S O0 sss*s*os * +

. + ... . + ....X . .. . + ... . + ... . + ... . + ... . + ... . + ... . + ... . + ... . + ... . + .. . . + ... . + . 7.50 0 22.5 0 37.5 0 52.5 0 67.5 0 82.5 0 97.5 0 0.00 0 15.0 0 30.0 0 45.0 0 60.0 0 75.0 0 90.0 0 105. 0

FIGUR E F-2c . Coefficien t o f variatio n(geometri c stnadar ddeviatio n divide db y

th e annua l geometri c mean ) versu s p .

;i4 5SYMBO L COUN T MEA N ST.DEV . 

## E
* 8 .0000. 0 * 16.000 0 •24.0.U'0 0 *32.000 0 •40.000 0 •48.000 0 •56.000 0 •64.000 0 * 7 2.000 0 * 80.000 0 * 33.000 0 * 9 6 .000. 0 * 104.00 0 * 112.00 0 •120.00 0 •1 2 8.00 0 •136.00 0 •144.00 0 •1 5 2.00 0 •160.00 0 •163.00 0 •1 7 6.00 0 •184.00 0 •192.00 0 •200.00 0 •203.00 0 •216.00 0 •2 2 4.00 0 •232.00 0
• 2 40.00 0 •2 4 8.00 0 •256.00 0 •264.00 0 •272.00 0
• 2 80.00 0 *288.00 0 •296.00 0 •304.00 0 5 1 0 EAC H 1 5 SYMBO L 2 0 2 5 A 229 5

REPRESENT S 3 0 3 5 4 0 1 .48 9 1 4 5 9 . 43 8 OBSERVATIO N 5 0 5 5 6 0 6 5 7 0 7 5 8 0 +AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA * +AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA A +AAAAAAAAAAAAAAA A +AAAAAA A + A A +AAA + AAA A +A A + + A A + AA + AA + +
• + + + + + + + + + + + + + + A + + + + A + + + + + 5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5 5 0 5 5 6 0 6 5 7 0 7 5 8 0 FREQUENC Y IN T 219 6 5 7 1 6 7 2 3 4 2 0 2 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 CUM . 219 6 225 3 226 9 227 6 227 8 228 1 228 5 228 7 228 7 228 9 229 1 229 3 229 3 229 3 229 3 229 3 229 3 229 3 229 3 229 3 229 3 229 3 229 3 229 3 229 3 229 3 229 3 229 3 229 4 229 4 229 4 229 4 229 5 229 5 229 5 229 5 229 5 229 5 PERCENTAG E INT . 95. 7 2. 5 0. 7 0. 3 0 . 1 0 . 1 0. 2 0 . 1 0. 0 0 . 1 0 . 1 0 . 1 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 CUM . 95. 7 98. 2 98. 9 99. 2 99. 3 99. 4 99. 6 99. 7 99. 7 99. 7 99. 8 99. 9 99. 9 99. 9 99. 9 99. 9 99. 9 99. 9 99. 9 99. 9 99. 9 99. 9 99. 9 99. 9 99. 9 99. 9 99. 9 99. 9 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0
Numbe r o f Site s

FIGUR E F-3a . Histogra mo f th e numbe r o f estimate d primar yexceedance s (E ) fo r site s reportin g TS P concentration s in 1981 : SLAM S (includin g NAMS) . 1 3 1 4 6

## E

* 8.00.00 0 •16.00.0 0 * 2 4.000 0 * 3 2.00.0 0 * 40.000 0 * 4 8.000 0 •56.000 0
• 6 4.000 0 •72.000 0 •80.000 0
• 8 8.000 0 •96.000 0 •104.00 0 •112. 00 0 •120.00 0 •1 2 8.00 0 •136.00 0 •14 4.00 0 •152.00 0 •160.00 0 •168.00 0 •176.1.0 0 •184.00 0 •192.00 0 •200.00 0 •203.0.0 0 •216.00 0 •2 2 4.00 0 •232.00 0 •240.00 0 •243.00 0 •2 5 5.00 0 •264 . .70 0
• 2 7 2.00 0 •23 0
. C, P<]
• 2 8B.00 0
• 2 30.00 0 •304.00 0 5 1 0 EAC H 1 5 SYMBO L

SYMBO L 2 0 2 5 B COUN T

137 2 REPRESENT S 3 0 3 5 4 0 MEA N 1 .48 3 1 4 5 ST.DE V

8 . . 14 4 OBSERVATIO N 5 0 5 5 6 0 6 5 7 0 7 5 8 0 +BBBBBBBBBBBBBBBBBBBBBBBBB3BBBBBBBBBBB8BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB * +BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB B +BBBBBBBBB B +BBBBB B + BBB B + BB B + B + + B B + B + B + + + B + B + + B + + + + + + + + + + + + + + + + + + + + + 5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5 5 0 5 5 6 0 6 5 7 0 7 5 8 0 FREQUENC Y INT . 130 2 3 9 1 0 6 4 3 1 0 2 1 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 CUM . 130 2 134 1 135 1 135 7 136 1 136 4 136 5 136 5 136 7 136 8 136 9 136 9 136 9 137 0 1 3 7 1 137 1 137 2 137 2 137 2 137 2 137 2 137 2 137 2 137 2 137 2 137 2 137 2 1 3 7 2 137 2 137 2 137 2 137 2 137 2 137 2 137 2 137 2 137 2 137 2 PERCENTAG E INT . 94. 9 2. 8 0. 7 0. 4 0. 3 0. 2 0 . 1 0. 0 0 . 1 0 . 1 0 . 1 0. 0 0. 0 0 . 1 0 . 1 0. 0 0 . 1 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 CUM . 94. 9 97. 7 98. 5 98. 9 99. 2 99. 4 99. 5 99. 5 99. 6 99. 7 99. 8 99. 8 99. 8 99. 9 99. 9 99. 9 100. 0 100. 0 100. 0 100. 0 100.0 100. 0 1 00. 0 1 00. 0 100. 0 100. 0 100. 0 100. 0 100.0 1 00.0 1 0 0 . 0 100. 0 100.0 1 00.0 1 00. 0 100.0 100.0 100.0
Numbe r o f Site s FIGURE F-3b . Othe r sites
. 8 ' j I '!< >P.6 7

SLAM S OTHE R N = 366 7 COR = .012 5 SYMBOL= S

SYMBOL= 0

MEA N 15.76 6 1 .486 7 ST.DEV .

12.23 3 8.974 5 REGRESSIO N

X = .0I703*Y + Y = .00919*X + LIN E RES.MS .

15.74 1 149.6 6 1.341 8 80.55 1

28 0 +

24 5 +

2i o +

17 5

14 0

10 5

70 .

35 .

0. 0 0 ,X+ .

0 0 s s 0 O S * S s s 3 0 O O S 0 0 s o s o 0 osss* * o* * s*** 0 s s

s o 0 0 s s s s ***** * sss * s 00 0 O S s S 0 0 0 Y**« ftft** ******«*****Q**Qs o S0*0* 0 * 0 *0*0S*0*00 * *0*00 0 *S**S*OS * Y + ... . + . . ..X . .. . + ... . + ... . + ... . + ... . + ... . + ... . + ... . + ... . + ... . + ... . + ... . + . 7.50 0 22.5 0 37.5 0 52.5 0 67.5 0 82.5 0 97.5 0 00 0 15.0 0 30.0 0 45.0 0 60.0 0 75.0 0 90.0 0 105. 0

FIGUR E F-4 . Scatte r plo t o f th e percentag e o f dat a completenes s (p ) versu s th e numbe r o f estimate d exceedance s (E ) fo r site s reportin g TS P concentration s i n 1981 .

8314 6TABL E F-3 . Frequencytabl e o f the percentag e o f tota l day s recorde d in 198 1 (p )

versu s expecte d primar yexceedance s (E ) fo r site s reporting 24-hou r TS P averages .

8314 6 SS - SX < = 10.5 X< = 13 X< - 14.5 X 15 % 15.5 X 16X 16 . 5 X 17 X< - 100 X TOTA L 0 1 - 1 0 < = 2 0 < - 26 0 TOTA L 33 9

0 2 7 34 8 32 6

7 1 6 6 35 5 30 8

2 8 3 8 34 7 30 8

2 7 8 5 34 8 23 1

1 3 2 2 24 8 36 7

2 3 1 0 3 40 3 42 6

2 3 6 5 46 0 54 2

2 1 1 2 8 58 3 21 9

1 7 1 0 3 24 9 25 7

5 2 9 8 32 6 332 3

2 1 1 7 8 5 5 366 7P.6 9



### Appendi xG SUMMAR YO F CURREN T DATA

COMPLETENES S CRITERI A

8314 6 5P.7 0

APPENDI XG SUMMARYO

F CURREN T DAT A

COMPLETENES S CRITERI A

The presen t dat a completenes s criteri a use dt o establis hvali dai r qualit y summar y statistic s vary

b y us e an dorganization . Table s G- l throug hG- 3 summariz e criteri a fo r NADB , th e Californi a Ai r Resource s Board , andth e Indian a Ai r Pollutio nContro l Division . Inaddition , th e EP A Trend s Repor t (EPA , 1983 ) require s 5 0 percen t o r 438 0 hourl y observation s pe r yea r (or insome case s 400 0 hour s pe r yea r i s used) .
The "EPA

fac t book " i nth e Trend s Report , whic hsummarize s ai r qualit y level s inStandar dMetropolita nStatistica l Areas , use s th e same criteri a a s inth e Trend s Repor t bu t note s i f th e volume o f annua l dat a i s less . Summarie s fro

m intermitten t measurement s base do nles s tha n3 0 day s o r les s tha nth e NAD B criteri a ar e als o noted .

REFERENCE S

CARB . 1983 . Californi a Ai r Qualit y Data , Summary

o f 198 2 Ai r Qualit y Data , Gaseou s an

dParticulat e Pollutants . Californi a Ai r Resource s Board , Sacramento , CA . EPA . 1977 . Aero s Manua l Serie s Volume III : Summary an dRetrieval . U.S , Environmenta l Protectio nAgency , Researc hTriangl e Park , N C (EPA 450/2-76-009a) . EPA . 1979a . "Implementatio nPlan s fo r Lea dNationa l Ambien t Ai r Qualit y Standards. " 4 3 Federa l Register , 46266 .

8314 6 5P.7 1



### EPA . 1979b . Guidelin e fo r LeadMonitorin ginth e Vicinit y o f Poin t Sources . U.S . Environmenta l ProtectionAgenc y (EPA-450/4-01-006) . EPA . 1983 . Nationa l Ai r Qualit y an dEmissio nTrend s Report , 1981 . U.S , Environmenta l ProtectionAgency , Researc hTriangl e Park , NC . EPA . 1984 . "Propose dRevision s t o th e Nationa l Ambien t Ai r Qualit y Standard s fo r Particulat e Matter. " 4 9 Federa l Register , 10434 .

8314 6 5P.7 2

TABLE G-l

. Nationa l Aerometri cDat a Bank' s criteri a fo rvalidit yo fdata

. (Source : EPA , 1977 )
Continuou s Samplin g (1 , 2 , an d 4-hou rdata ) Quarterl ystatistics--75 % o r164 2 hour s Annua l statistics--75 % o r657 0 hour s Intermitten tSamplin g (24-hou rdata ) * Quarterl ystatistics—fiv e sample s pe rquarte r ( I fon e mont h has n o values , a tleas ttw o value s i n othe rmonths ) Annua l statistics—fou rvali d quarter s Not e tha t1 2 sample s pe rquarte rar e require d t o demonstrat e attainmen to fth e NAAQ S fo rlea d (EPA , 1979a , b)

. Thi s requiremen ti s als o unde r consideratio n fo rparticulate s (EPA , 1984)

.

83146 T 8P.7 3

TABLE G-2

. Californi a Ai rResource s Board' s criteri a fo rrepresentativenes s o fai rqualit ymeasurement s an d statistics

. (Source : CARB , 1983 ) 

### Representativ e Calenda r Statisti c / 

### Yea r J

( / 

### Quarte r j

(
/ 

## Mont h1

( 

### Samplin gTime

Perio d 

## 4 representativ e calenda r quarter s 2 4 hour s 

## O r

< 2 4 hour s
2 4 hour s 

## O r

< 2 4 hour s

Da y



### Mea no f N Hou r Perio d 1-hou r O r 2-hour ; o r 3-hour ; o r 24-hou r H_ 

### 2 4 8 6 4 3 2 1 Basi s o f Statisti c o r

Requiremen t Numbe r o f Representativ e Hour s o r Day s Require d

3 representativ e month s Base dona dail y statistic ; or
Base donhourl y

statistic s 4 o r mor e 24-hou r sample s Base do na dail y statistic ; o r Base donal l hourl y statistics ; o r Base donal l 2-hour statistics ; o r Base donal l 3-hour statistic s No mor e than2 consecutiv e sample s missin gi neac h 1/ 3 da y (hour s 0 thr u7 , 8 thru15 , 1 6 thru23 ) 6 9 o r mor e calenda r day s 1,63 8 or mor e hour s

2 3 o r mor e calenda r day s 54 8 o r mor e hour s
27 4 o r mor e 2-hou r statistic s 18 3 o r mor e 3-hou r statistic s
6 o r mor e hour s i n eac h1/ 3 day
1 1 o r mor e sample s 7 o r mor e sample s 2 2 bu t no t mor e than2 6 hour s o f sampling Numbe r o f Sample s Neede d 2 2 o r mor e hourl y

sample s 7 o r mor e hourl y

sample s 5 o r mor e hourl y

sample s 4 hourl y

sample s 3 hourl y

sample s 2 hourl y sample s 3 0 minute s o r mor e o f sampling *
* AR B Qualit y

Assuranc e Manua l Sectio n2.0.23 .

8314 6P.7 4

TABL E G-3 . Indian a Ai r Pollutio nContro l Division' s criteri a fo r completenes s o f data . (Source : Buch , 1983 ) Tim e Interva l Minimum Numbe r o f Vali dObservation s
A . Continuou s Hou r Day Mont h Quarte r Yea r Three-Hou r Running Averag e Eight-Hou r Running Averag e A t leas t forty-fiv e (45 ) minutes * A t leas t eightee n (18 ) hour s A t leas t twenty-on e (21 ) day s Thre e (3 ) month s A t leas t nin e (9 ) month s wit h a t leas t two (2 ) month s pe r quarte r Thre e (3 ) consecutiv e hour s Six.(6 ) hour s ou t o f eigh t (8 ) consecutiv e hour s
B

. Six-Da ySamplin g Schedul e Quarte r

Yea r A t leas t te n (10 ) sample s unles s on e (1 ) mont hha s zer o (0 ) vali d samples , the n th e remaining two (2 ) month s mus t hav e a t leas t fou r (4 ) sample s each . A t leas t nin e (9 ) month s wit ha t leas t two (2 ) month s pe r quarte r an d a t leas t forty-fiv e (45 ) samples . C

. Dail ySamplin g Schedul e Quarte r

Yea r A t leas t sixt y(60 ) sample s unles s on e (1 ) mont hha s zer o (0 ) vali d samples , the n eac hremaining mont hmus t have a t leas t twenty-fou r (24 ) samples . A t leas t nin e (9 ) month s wit n a t leas t two (2 ) month s pe r quarte r an d a t leas t two-hundre d seventy-thre e (273 ) sample s

*I f an hou r include s the dail yzero/span sequence , th e hou r i s considere d vali d i f a t leas t fiftypercen t (50.0% ) o f the hou r i s valid
.

8314 6